{"abstract":"A bad fix sharing a timestamp with a good fix causes both to be dropped.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"Billable distance from GPS pings [t seconds, x m, y m, accuracy m, paused]. Sort by time (stable); drop pings with accuracy worse than max_acc; among remaining pings with the same timestamp keep the first. Walk the kept pings with an anchor: segment length is the floored Euclidean distance (isqrt); if it implies a speed above max_speed m/s the ping is discarded and the anchor stays; otherwise the segment is billed unless either endpoint is paused, and the anchor moves. Return total meters.","contract_signature":"pings, max_acc, max_speed","evaluation_group":"w2-ride-hailing-fare-surge-gps-trace-distance","failed_approach":"Removing deduplication keeps zero-duration segments that trip the speed filter.","family":"w2-ride-hailing-fare-surge-gps-trace-distance-accuracy-before-dedupe","id":"FA-85526","implementations":{"attempt":{"sha256":"493d03e7404d5fe8ffce858d5e8090e2ba4d13a1da2b82767ac66dca2b6d0a0d","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pings, max_acc, max_speed):\n    kept = []\n    seen = set()\n    for p in sorted(pings, key=lambda p: p[0]):\n        if p[3] > max_acc:\n            continue\n        kept.append(p)\n    total = 0\n    anchor = None\n    for p in kept:\n        if anchor is None:\n            anchor = p\n            continue\n        d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)\n        dt = p[0] - anchor[0]\n        if d > max_speed * dt:\n            continue\n        if not (p[4] or anchor[4]):\n            total += d\n        anchor = p\n    return total\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: accuracy before dedupe',\n   [[[10, 60, -15, 20, False], [20, 120, -30, 5, False], [20, 165, -45, 20, False], [30, 565, -12, 10, False],\n     [40, 610, 8, 25, False], [40, 655, 41, 20, False]],\n    20, 30],\n   600),\n  ('partial repair probe: accuracy before dedupe',\n   [[[20, 135, 18, 20, False], [10, 60, -15, 60, False], [15, 105, 18, 5, False], [30, 640, 69, 20, False],\n     [45, 640, 109, 20, False], [25, 580, 36, 5, False], [40, 640, 89, 10, False], [25, 180, 51, 10, False]],\n    20, 30],\n   559),\n  ('second regression',\n   [[[5, 60, 0, 20, False], [10, 120, 20, 25, True], [15, 520, 20, 20, True], [20, 580, 5, 25, False],\n     [30, 580, -10, 60, False], [30, 640, -25, 10, False], [40, 670, -5, 60, False]],\n    20, 30],\n   580),\n  ('normal control 1', [[[0, 60, 0, 10, False], [10, 90, -15, 20, True], [10, 490, -15, 10, False]], 20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, 0, 10, False], [10, 800, 20, 20, False], [20, 845, 5, 20, False], [30, 1245, 5, 10, False],\n     [40, 1305, 5, 5, True], [45, 1350, 25, 60, False], [50, 1380, 25, 20, False]],\n    20, 30],\n   445),\n  ('normal control 3', [[[10, 105, 5, 5, False], [15, 505, 25, 20, False], [5, 45, -15, 20, False]], 20, 30],\n   63),\n  ('normal control 4',\n   [[[20, 60, 40, 20, False], [10, 30, 0, 25, False], [20, 60, 20, 5, False], [25, 460, 40, 60, True],\n     [0, 30, 0, 60, False]],\n    20, 30],\n   0)],\n [('regression: accuracy before dedupe',\n   [[[45, 195, 152, 60, True], [10, 75, 53, 60, True], [35, 195, 119, 25, False], [25, 165, 119, 20, False],\n     [25, 195, 119, 25, False], [20, 135, 86, 10, False], [10, 45, 33, 20, False]],\n    20, 30],\n   148),\n  ('partial repair probe: accuracy before dedupe',\n   [[[25, 610, -25, 10, False], [30, 1040, -40, 10, False], [10, 45, -15, 25, False],\n     [25, 120, -30, 60, False], [25, 210, -25, 20, False], [25, 165, -10, 25, False],\n     [20, 75, -30, 10, False], [25, 640, -25, 10, True]],\n    20, 30],\n   0),\n  ('second regression',\n   [[[40, 150, -9, 5, False], [25, 120, -12, 60, False], [5, 30, -15, 25, True], [35, 150, 6, 20, False],\n     [20, 120, -45, 60, False], [30, 120, 21, 10, False], [5, 75, -30, 20, False],\n     [15, 120, -45, 10, False]],\n    20, 30],\n   161),\n  ('normal control 1',\n   [[[15, 520, 33, 60, False], [25, 550, 33, 60, False], [10, 460, 0, 25, True], [15, 490, 0, 20, True],\n     [5, 400, 0, 25, False]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, 0, 20, False], [5, 400, 0, 10, False], [10, 800, -15, 5, True], [15, 845, 5, 10, True]], 20,\n    30],\n   0),\n  ('normal control 3',\n   [[[0, 430, 40, 20, False], [15, 860, 80, 10, True], [20, 860, 100, 5, False], [5, 830, 40, 10, True],\n     [10, 830, 60, 25, False], [20, 1260, 100, 20, False], [0, 30, 20, 60, False]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[0, 30, 20, 5, False], [5, 75, 53, 10, False], [15, 135, 53, 20, True], [20, 165, 73, 25, False],\n     [30, 210, 93, 60, False]],\n    20, 30],\n   55)],\n [('regression: accuracy before dedupe',\n   [[[10, 430, 53, 25, False], [10, 460, 86, 20, False], [5, 400, 20, 20, False], [10, 460, 53, 25, False]],\n    20, 30],\n   89),\n  ('partial repair probe: accuracy before dedupe',\n   [[[5, 445, 20, 5, True], [20, 565, 91, 20, False], [20, 520, 58, 20, False], [10, 475, 38, 20, False],\n     [0, 400, 0, 20, False], [5, 475, 5, 60, False], [30, 995, 124, 5, True], [30, 595, 91, 10, False]],\n    20, 30],\n   104),\n  ('second regression',\n   [[[10, 400, -15, 5, False], [15, 400, -30, 25, False], [15, 460, 3, 20, False]], 20, 30], 62),\n  ('normal control 1',\n   [[[15, 135, 33, 20, False], [15, 105, 0, 60, False], [20, 535, 53, 20, False], [25, 980, 38, 5, False],\n     [10, 45, 0, 25, False], [20, 580, 38, 20, True]],\n    20, 30],\n   0),\n  ('normal control 2', [[[5, 0, 33, 5, False], [10, 400, 33, 20, False], [15, 430, 66, 60, False]], 20, 30],\n   0),\n  ('normal control 3', [[[5, 60, 20, 60, False], [15, 105, 53, 20, False], [20, 165, 53, 5, False]], 20, 30],\n   60),\n  ('normal control 4',\n   [[[5, 60, 33, 10, False], [15, 120, 18, 20, False], [25, 120, 3, 60, False], [30, 180, 23, 10, False],\n     [40, 180, 43, 60, False], [45, 180, 63, 60, False]],\n    20, 30],\n   121)],\n [('regression: accuracy before dedupe',\n   [[[5, 0, 20, 25, False], [5, 60, 40, 5, False], [10, 120, 25, 5, False]], 20, 30], 61),\n  ('partial repair probe: accuracy before dedupe',\n   [[[25, 475, -30, 5, False], [45, 1040, 36, 60, False], [15, 445, -30, 25, False], [30, 875, 3, 60, False],\n     [45, 1100, 56, 10, True], [45, 980, 36, 20, False], [35, 920, 3, 10, False], [5, 45, -15, 20, False]],\n    20, 30],\n   430),\n  ('second regression',\n   [[[15, 490, 23, 20, False], [10, 430, 38, 25, False], [10, 430, 5, 5, False], [5, 400, -15, 20, False],\n     [15, 520, 56, 5, False]],\n    20, 30],\n   98),\n  ('normal control 1',\n   [[[15, 135, 5, 10, False], [20, 195, 25, 25, False], [10, 105, -15, 60, False], [5, 45, -15, 20, False],\n     [0, 45, -15, 25, True]],\n    20, 30],\n   92),\n  ('normal control 2',\n   [[[10, 60, 18, 5, False], [5, 60, 33, 60, False], [30, 120, 23, 20, False], [20, 120, 3, 60, False],\n     [25, 120, 23, 20, True]],\n    20, 30],\n   0),\n  ('normal control 3',\n   [[[0, 30, 0, 20, False], [10, 90, -15, 20, False], [20, 90, -15, 60, False], [25, 135, 18, 20, False],\n     [25, 165, 3, 20, False], [25, 195, 36, 25, False], [30, 225, 56, 60, False]],\n    20, 30],\n   116),\n  ('normal control 4',\n   [[[0, 30, 20, 25, False], [0, 430, 5, 25, False], [0, 475, -10, 60, True], [0, 475, -25, 5, False],\n     [10, 475, -5, 25, True], [15, 505, 15, 60, True], [15, 550, 35, 20, True], [20, 610, 20, 10, True]],\n    20, 30],\n   0)],\n [('regression: accuracy before dedupe',\n   [[[5, 60, 33, 20, False], [10, 90, 99, 25, False], [10, 60, 66, 10, False]], 20, 30], 33),\n  ('partial repair probe: accuracy before dedupe',\n   [[[20, 800, 40, 5, False], [35, 1290, 93, 20, False], [10, 400, 20, 20, False], [40, 1290, 126, 20, False],\n     [20, 845, 40, 10, True], [30, 890, 73, 5, True], [20, 400, 40, 5, False], [5, 400, 20, 5, False]],\n    20, 30],\n   0),\n  ('second regression', [[[10, 0, 20, 5, False], [15, 60, 38, 25, False], [15, 60, 53, 20, False]], 20, 30],\n   68),\n  ('normal control 1',\n   [[[15, 505, -30, 10, True], [20, 905, -45, 60, True], [5, 30, -15, 20, False], [5, 60, -30, 25, False],\n     [10, 105, -30, 20, False]],\n    20, 30],\n   76),\n  ('normal control 2',\n   [[[10, 45, 20, 25, False], [15, 45, 20, 10, False], [20, 75, 53, 60, False], [25, 75, 86, 10, False],\n     [30, 105, 71, 25, False]],\n    20, 30],\n   72),\n  ('normal control 3',\n   [[[10, 0, 0, 20, False], [20, 30, -15, 20, False], [25, 430, -30, 25, False], [35, 475, -30, 5, False],\n     [35, 875, -10, 10, False]],\n    20, 30],\n   478),\n  ('normal control 4',\n   [[[45, 640, 124, 20, False], [30, 550, 71, 60, False], [5, 45, 66, 20, True], [25, 505, 86, 25, False],\n     [15, 105, 66, 60, False], [5, 0, 33, 10, False], [40, 580, 91, 20, False], [55, 700, 109, 25, True]],\n    20, 30],\n   68)]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"826d302cf7c2f94b699ef24a1b8e96026b05637da630f13d1ec028f189cc2497","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pings, max_acc, max_speed):\n    kept = []\n    seen = set()\n    for p in sorted(pings, key=lambda p: p[0]):\n        if p[0] in seen:\n            continue\n        seen.add(p[0])\n        if p[3] > max_acc:\n            continue\n        kept.append(p)\n    total = 0\n    anchor = None\n    for p in kept:\n        if anchor is None:\n            anchor = p\n            continue\n        d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)\n        dt = p[0] - anchor[0]\n        if d > max_speed * dt:\n            continue\n        if not (p[4] or anchor[4]):\n            total += d\n        anchor = p\n    return total\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: accuracy before dedupe',\n   [[[10, 60, -15, 20, False], [20, 120, -30, 5, False], [20, 165, -45, 20, False], [30, 565, -12, 10, False],\n     [40, 610, 8, 25, False], [40, 655, 41, 20, False]],\n    20, 30],\n   600),\n  ('partial repair probe: accuracy before dedupe',\n   [[[20, 135, 18, 20, False], [10, 60, -15, 60, False], [15, 105, 18, 5, False], [30, 640, 69, 20, False],\n     [45, 640, 109, 20, False], [25, 580, 36, 5, False], [40, 640, 89, 10, False], [25, 180, 51, 10, False]],\n    20, 30],\n   559),\n  ('second regression',\n   [[[5, 60, 0, 20, False], [10, 120, 20, 25, True], [15, 520, 20, 20, True], [20, 580, 5, 25, False],\n     [30, 580, -10, 60, False], [30, 640, -25, 10, False], [40, 670, -5, 60, False]],\n    20, 30],\n   580),\n  ('normal control 1', [[[0, 60, 0, 10, False], [10, 90, -15, 20, True], [10, 490, -15, 10, False]], 20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, 0, 10, False], [10, 800, 20, 20, False], [20, 845, 5, 20, False], [30, 1245, 5, 10, False],\n     [40, 1305, 5, 5, True], [45, 1350, 25, 60, False], [50, 1380, 25, 20, False]],\n    20, 30],\n   445),\n  ('normal control 3', [[[10, 105, 5, 5, False], [15, 505, 25, 20, False], [5, 45, -15, 20, False]], 20, 30],\n   63),\n  ('normal control 4',\n   [[[20, 60, 40, 20, False], [10, 30, 0, 25, False], [20, 60, 20, 5, False], [25, 460, 40, 60, True],\n     [0, 30, 0, 60, False]],\n    20, 30],\n   0)],\n [('regression: accuracy before dedupe',\n   [[[45, 195, 152, 60, True], [10, 75, 53, 60, True], [35, 195, 119, 25, False], [25, 165, 119, 20, False],\n     [25, 195, 119, 25, False], [20, 135, 86, 10, False], [10, 45, 33, 20, False]],\n    20, 30],\n   148),\n  ('partial repair probe: accuracy before dedupe',\n   [[[25, 610, -25, 10, False], [30, 1040, -40, 10, False], [10, 45, -15, 25, False],\n     [25, 120, -30, 60, False], [25, 210, -25, 20, False], [25, 165, -10, 25, False],\n     [20, 75, -30, 10, False], [25, 640, -25, 10, True]],\n    20, 30],\n   0),\n  ('second regression',\n   [[[40, 150, -9, 5, False], [25, 120, -12, 60, False], [5, 30, -15, 25, True], [35, 150, 6, 20, False],\n     [20, 120, -45, 60, False], [30, 120, 21, 10, False], [5, 75, -30, 20, False],\n     [15, 120, -45, 10, False]],\n    20, 30],\n   161),\n  ('normal control 1',\n   [[[15, 520, 33, 60, False], [25, 550, 33, 60, False], [10, 460, 0, 25, True], [15, 490, 0, 20, True],\n     [5, 400, 0, 25, False]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, 0, 20, False], [5, 400, 0, 10, False], [10, 800, -15, 5, True], [15, 845, 5, 10, True]], 20,\n    30],\n   0),\n  ('normal control 3',\n   [[[0, 430, 40, 20, False], [15, 860, 80, 10, True], [20, 860, 100, 5, False], [5, 830, 40, 10, True],\n     [10, 830, 60, 25, False], [20, 1260, 100, 20, False], [0, 30, 20, 60, False]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[0, 30, 20, 5, False], [5, 75, 53, 10, False], [15, 135, 53, 20, True], [20, 165, 73, 25, False],\n     [30, 210, 93, 60, False]],\n    20, 30],\n   55)],\n [('regression: accuracy before dedupe',\n   [[[10, 430, 53, 25, False], [10, 460, 86, 20, False], [5, 400, 20, 20, False], [10, 460, 53, 25, False]],\n    20, 30],\n   89),\n  ('partial repair probe: accuracy before dedupe',\n   [[[5, 445, 20, 5, True], [20, 565, 91, 20, False], [20, 520, 58, 20, False], [10, 475, 38, 20, False],\n     [0, 400, 0, 20, False], [5, 475, 5, 60, False], [30, 995, 124, 5, True], [30, 595, 91, 10, False]],\n    20, 30],\n   104),\n  ('second regression',\n   [[[10, 400, -15, 5, False], [15, 400, -30, 25, False], [15, 460, 3, 20, False]], 20, 30], 62),\n  ('normal control 1',\n   [[[15, 135, 33, 20, False], [15, 105, 0, 60, False], [20, 535, 53, 20, False], [25, 980, 38, 5, False],\n     [10, 45, 0, 25, False], [20, 580, 38, 20, True]],\n    20, 30],\n   0),\n  ('normal control 2', [[[5, 0, 33, 5, False], [10, 400, 33, 20, False], [15, 430, 66, 60, False]], 20, 30],\n   0),\n  ('normal control 3', [[[5, 60, 20, 60, False], [15, 105, 53, 20, False], [20, 165, 53, 5, False]], 20, 30],\n   60),\n  ('normal control 4',\n   [[[5, 60, 33, 10, False], [15, 120, 18, 20, False], [25, 120, 3, 60, False], [30, 180, 23, 10, False],\n     [40, 180, 43, 60, False], [45, 180, 63, 60, False]],\n    20, 30],\n   121)],\n [('regression: accuracy before dedupe',\n   [[[5, 0, 20, 25, False], [5, 60, 40, 5, False], [10, 120, 25, 5, False]], 20, 30], 61),\n  ('partial repair probe: accuracy before dedupe',\n   [[[25, 475, -30, 5, False], [45, 1040, 36, 60, False], [15, 445, -30, 25, False], [30, 875, 3, 60, False],\n     [45, 1100, 56, 10, True], [45, 980, 36, 20, False], [35, 920, 3, 10, False], [5, 45, -15, 20, False]],\n    20, 30],\n   430),\n  ('second regression',\n   [[[15, 490, 23, 20, False], [10, 430, 38, 25, False], [10, 430, 5, 5, False], [5, 400, -15, 20, False],\n     [15, 520, 56, 5, False]],\n    20, 30],\n   98),\n  ('normal control 1',\n   [[[15, 135, 5, 10, False], [20, 195, 25, 25, False], [10, 105, -15, 60, False], [5, 45, -15, 20, False],\n     [0, 45, -15, 25, True]],\n    20, 30],\n   92),\n  ('normal control 2',\n   [[[10, 60, 18, 5, False], [5, 60, 33, 60, False], [30, 120, 23, 20, False], [20, 120, 3, 60, False],\n     [25, 120, 23, 20, True]],\n    20, 30],\n   0),\n  ('normal control 3',\n   [[[0, 30, 0, 20, False], [10, 90, -15, 20, False], [20, 90, -15, 60, False], [25, 135, 18, 20, False],\n     [25, 165, 3, 20, False], [25, 195, 36, 25, False], [30, 225, 56, 60, False]],\n    20, 30],\n   116),\n  ('normal control 4',\n   [[[0, 30, 20, 25, False], [0, 430, 5, 25, False], [0, 475, -10, 60, True], [0, 475, -25, 5, False],\n     [10, 475, -5, 25, True], [15, 505, 15, 60, True], [15, 550, 35, 20, True], [20, 610, 20, 10, True]],\n    20, 30],\n   0)],\n [('regression: accuracy before dedupe',\n   [[[5, 60, 33, 20, False], [10, 90, 99, 25, False], [10, 60, 66, 10, False]], 20, 30], 33),\n  ('partial repair probe: accuracy before dedupe',\n   [[[20, 800, 40, 5, False], [35, 1290, 93, 20, False], [10, 400, 20, 20, False], [40, 1290, 126, 20, False],\n     [20, 845, 40, 10, True], [30, 890, 73, 5, True], [20, 400, 40, 5, False], [5, 400, 20, 5, False]],\n    20, 30],\n   0),\n  ('second regression', [[[10, 0, 20, 5, False], [15, 60, 38, 25, False], [15, 60, 53, 20, False]], 20, 30],\n   68),\n  ('normal control 1',\n   [[[15, 505, -30, 10, True], [20, 905, -45, 60, True], [5, 30, -15, 20, False], [5, 60, -30, 25, False],\n     [10, 105, -30, 20, False]],\n    20, 30],\n   76),\n  ('normal control 2',\n   [[[10, 45, 20, 25, False], [15, 45, 20, 10, False], [20, 75, 53, 60, False], [25, 75, 86, 10, False],\n     [30, 105, 71, 25, False]],\n    20, 30],\n   72),\n  ('normal control 3',\n   [[[10, 0, 0, 20, False], [20, 30, -15, 20, False], [25, 430, -30, 25, False], [35, 475, -30, 5, False],\n     [35, 875, -10, 10, False]],\n    20, 30],\n   478),\n  ('normal control 4',\n   [[[45, 640, 124, 20, False], [30, 550, 71, 60, False], [5, 45, 66, 20, True], [25, 505, 86, 25, False],\n     [15, 105, 66, 60, False], [5, 0, 33, 10, False], [40, 580, 91, 20, False], [55, 700, 109, 25, True]],\n    20, 30],\n   68)]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic toy pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-ride-hailing-fare-surge-gps-trace-distance-accuracy-before-dedupe","generated_at":"2026-09-29T14:50:41.188732+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.","root_cause":"Timestamp deduplication runs before the accuracy filter.","sha256":"9e63f04a0a3adef6140d0ae54584b40a8b2327d6531d8da6e56a50a34db7aeba","title":"Inaccurate duplicate hides a good ping · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":41.612,"exit_code":1,"observations":[{"actual":600,"check":"regression: accuracy before dedupe","expected":600,"passed":true},{"actual":548,"check":"partial repair probe: accuracy before dedupe","expected":559,"passed":false},{"actual":580,"check":"second regression","expected":580,"passed":true},{"actual":0,"check":"normal control 1","expected":0,"passed":true},{"actual":445,"check":"normal control 2","expected":445,"passed":true},{"actual":63,"check":"normal control 3","expected":63,"passed":true},{"actual":0,"check":"normal control 4","expected":0,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: accuracy before dedupe\", \"actual\": 600, \"expected\": 600, \"passed\": true}, {\"check\": \"partial repair probe: accuracy before dedupe\", \"actual\": 548, \"expected\": 559, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 580, \"expected\": 580, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 445, \"expected\": 445, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 63, \"expected\": 63, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.623,"exit_code":1,"observations":[{"actual":61,"check":"regression: accuracy before dedupe","expected":600,"passed":false},{"actual":559,"check":"partial repair probe: accuracy before dedupe","expected":559,"passed":true},{"actual":0,"check":"second regression","expected":580,"passed":false},{"actual":0,"check":"normal control 1","expected":0,"passed":true},{"actual":445,"check":"normal control 2","expected":445,"passed":true},{"actual":63,"check":"normal control 3","expected":63,"passed":true},{"actual":0,"check":"normal control 4","expected":0,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: accuracy before dedupe\", \"actual\": 61, \"expected\": 600, \"passed\": false}, {\"check\": \"partial repair probe: accuracy before dedupe\", \"actual\": 559, \"expected\": 559, \"passed\": true}, {\"check\": \"second regression\", \"actual\": 0, \"expected\": 580, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 445, \"expected\": 445, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 63, \"expected\": 63, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}