{"abstract":"The SRM statistic is inflated for under-filled arms and deflated for over-filled ones.","category":"Experiment statistics","checks":8,"contract":"counts and weights are per-arm lists. Expected count = total * w / sum(weights). chi2 sums (observed - expected)^2 / expected over positive-weight arms; any unit in a zero-weight arm is an immediate mismatch [None, True]. df = number of positive-weight arms - 1; mismatch iff chi2 exceeds the alpha = 0.001 critical value (10.828, 13.816, 16.266, 18.467, 20.515 for df 1..5). No units or df < 1 -> [0.0, False]. Return [round(chi2, 6), mismatch].","evaluation_group":"w2-experiment-statistics-srm-chi-square","failed_approach":"Dividing by the total shrinks every term and hides real mismatches.","family":"w2-experiment-statistics-srm-chi-square-expected-denominator","id":"FA-74341","implementations":{"attempt":{"sha256":"2bb6209f88768e26e23f241f12049d477634f73ba44d5f383d74c6ef206c8999","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(counts, weights):\n    CRIT = {1: 10.828, 2: 13.816, 3: 16.266, 4: 18.467, 5: 20.515}\n    total = sum(counts)\n    wsum = sum(weights)\n    if total == 0:\n        return [0.0, False]\n    chi2 = 0.0\n    for o, w in zip(counts, weights):\n        if w == 0:\n            if o > 0:\n                return [None, True]\n            continue\n        e = total * w / wsum\n        chi2 += (o - e) ** 2 / total\n    df = sum(1 for w in weights if w > 0) - 1\n    if df < 1:\n        return [0.0, False]\n    return [round(chi2, 6), chi2 > CRIT[df]]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('balanced split within noise', [[5040, 4960], [50, 50]], [0.64, False]),\n  ('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 2', [[45, 53, 73, 18], [1, 50, 2, 50]], [2400.801481, True])],\n [('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 6', [[32, 944, 99], [1, 50, 2]], [95.793172, True])],\n [('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('arm count sample 9', [[495, 543], [1, 1]], [2.219653, False]),\n  ('arm count sample 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 17', [[2, 967], [1, 50]], [15.514737, True])],\n [('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('arm count sample 22', [[18, 19, 157], [3, 2, 50]], [27.553608, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, False]),\n  ('arm count sample 24', [[0, 0, 5018], [0, 0, 50]], [0.0, False])],\n [('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('no units yet', [[0, 0], [1, 1]], [0.0, False]),\n  ('arm count sample 29', [[274, 203, 4530], [3, 2, 50]], [2.512849, False]),\n  ('arm count sample 30', [[1029, 942, 3015], [1, 1, 3]], [4.252708, False]),\n  ('arm count sample 39', [[0, 105, 375, 486], [0, 1, 3, 3]], [24.086957, True])]]\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":"b295e0b248ecb6c16247f0962ea2b7e26247b0f1450b266e6fdbbfbbcce835dc","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(counts, weights):\n    CRIT = {1: 10.828, 2: 13.816, 3: 16.266, 4: 18.467, 5: 20.515}\n    total = sum(counts)\n    wsum = sum(weights)\n    if total == 0:\n        return [0.0, False]\n    chi2 = 0.0\n    for o, w in zip(counts, weights):\n        if w == 0:\n            if o > 0:\n                return [None, True]\n            continue\n        e = total * w / wsum\n        chi2 += (o - e) ** 2 / o\n    df = sum(1 for w in weights if w > 0) - 1\n    if df < 1:\n        return [0.0, False]\n    return [round(chi2, 6), chi2 > CRIT[df]]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('balanced split within noise', [[5040, 4960], [50, 50]], [0.64, False]),\n  ('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 2', [[45, 53, 73, 18], [1, 50, 2, 50]], [2400.801481, True])],\n [('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 6', [[32, 944, 99], [1, 50, 2]], [95.793172, True])],\n [('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('arm count sample 9', [[495, 543], [1, 1]], [2.219653, False]),\n  ('arm count sample 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 17', [[2, 967], [1, 50]], [15.514737, True])],\n [('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('arm count sample 22', [[18, 19, 157], [3, 2, 50]], [27.553608, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, False]),\n  ('arm count sample 24', [[0, 0, 5018], [0, 0, 50]], [0.0, False])],\n [('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('no units yet', [[0, 0], [1, 1]], [0.0, False]),\n  ('arm count sample 29', [[274, 203, 4530], [3, 2, 50]], [2.512849, False]),\n  ('arm count sample 30', [[1029, 942, 3015], [1, 1, 3]], [4.252708, False]),\n  ('arm count sample 39', [[0, 105, 375, 486], [0, 1, 3, 3]], [24.086957, True])]]\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"},"fixed":{"sha256":"d8a2244a07ed38aa1e564fc27f13a390125e05c8b22ef60a10633ea3772286c3","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(counts, weights):\n    CRIT = {1: 10.828, 2: 13.816, 3: 16.266, 4: 18.467, 5: 20.515}\n    total = sum(counts)\n    wsum = sum(weights)\n    if total == 0:\n        return [0.0, False]\n    chi2 = 0.0\n    for o, w in zip(counts, weights):\n        if w == 0:\n            if o > 0:\n                return [None, True]\n            continue\n        e = total * w / wsum\n        chi2 += (o - e) ** 2 / e\n    df = sum(1 for w in weights if w > 0) - 1\n    if df < 1:\n        return [0.0, False]\n    return [round(chi2, 6), chi2 > CRIT[df]]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('balanced split within noise', [[5040, 4960], [50, 50]], [0.64, False]),\n  ('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 2', [[45, 53, 73, 18], [1, 50, 2, 50]], [2400.801481, True])],\n [('unequal design weights are respected', [[2000, 1000], [2, 1]], [0.0, False]),\n  ('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('three arms uneven', [[3100, 3300, 3600], [1, 1, 1]], [38.0, True]),\n  ('arm count sample 1', [[493, 477], [3, 3]], [0.263918, False]),\n  ('arm count sample 6', [[32, 944, 99], [1, 50, 2]], [95.793172, True])],\n [('ratio weights not in percent', [[510, 490], [1, 1]], [0.4, False]),\n  ('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('arm count sample 9', [[495, 543], [1, 1]], [2.219653, False]),\n  ('arm count sample 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 17', [[2, 967], [1, 50]], [15.514737, True])],\n [('clear mismatch at alpha 0.001', [[5200, 4800], [1, 1]], [16.0, True]),\n  ('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('arm count sample 22', [[18, 19, 157], [3, 2, 50]], [27.553608, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, False]),\n  ('arm count sample 24', [[0, 0, 5018], [0, 0, 50]], [0.0, False])],\n [('moderate imbalance below the strict threshold', [[5100, 4900], [1, 1]], [4.0, False]),\n  ('traffic in a zero-weight arm is a mismatch', [[500, 500, 3], [1, 1, 0]], [None, True]),\n  ('drained arm does not loosen the threshold', [[5170, 4830, 0], [1, 1, 0]], [11.56, True]),\n  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, False]),\n  ('no units yet', [[0, 0], [1, 1]], [0.0, False]),\n  ('arm count sample 29', [[274, 203, 4530], [3, 2, 50]], [2.512849, False]),\n  ('arm count sample 30', [[1029, 942, 3015], [1, 1, 3]], [4.252708, False]),\n  ('arm count sample 39', [[0, 105, 375, 486], [0, 1, 3, 3]], [24.086957, True])]]\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 experiment-analysis model with a stipulated contract; results are rounded and are not a substitute for a validated statistics package. 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-experiment-statistics-srm-chi-square-expected-denominator","generated_at":"2026-09-29T14:48:55.877255+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"SRM checks are the first gate on any experiment readout; a broken check hides assignment bugs.","repair":"Divide each squared deviation by the expected count.","root_cause":"Each term is (o - e)^2 / o instead of dividing by the expected count.","sha256":"f40ddc19ea550177bef2ed51b70ddf3e684354855408c53b1c3597cce0a61b33","title":"Sample ratio mismatch check: Chi-square terms divide by the observed count · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":41.455,"exit_code":1,"observations":[{"actual":[0.32,false],"check":"balanced split within noise","expected":[0.64,false],"passed":false},{"actual":[0.0,false],"check":"unequal design weights are respected","expected":[0.0,false],"passed":true},{"actual":[0.2,false],"check":"ratio weights not in percent","expected":[0.4,false],"passed":false},{"actual":[8.0,false],"check":"clear mismatch at alpha 0.001","expected":[16.0,true],"passed":false},{"actual":[2.0,false],"check":"moderate imbalance below the strict threshold","expected":[4.0,false],"passed":false},{"actual":[12.666667,false],"check":"three arms uneven","expected":[38.0,true],"passed":false},{"actual":[0.131959,false],"check":"arm count sample 1","expected":[0.263918,false],"passed":false},{"actual":[72.010385,true],"check":"arm count sample 2","expected":[2400.801481,true],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"balanced split within noise\", \"actual\": [0.32, false], \"expected\": [0.64, false], \"passed\": false}, {\"check\": \"unequal design weights are respected\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"ratio weights not in percent\", \"actual\": [0.2, false], \"expected\": [0.4, false], \"passed\": false}, {\"check\": \"clear mismatch at alpha 0.001\", \"actual\": [8.0, false], \"expected\": [16.0, true], \"passed\": false}, {\"check\": \"moderate imbalance below the strict threshold\", \"actual\": [2.0, false], \"expected\": [4.0, false], \"passed\": false}, {\"check\": \"three arms uneven\", \"actual\": [12.666667, false], \"expected\": [38.0, true], \"passed\": false}, {\"check\": \"arm count sample 1\", \"actual\": [0.131959, false], \"expected\": [0.263918, false], \"passed\": false}, {\"check\": \"arm count sample 2\", \"actual\": [72.010385, true], \"expected\": [2400.801481, true], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":49.619,"exit_code":1,"observations":[{"actual":[0.640041,false],"check":"balanced split within noise","expected":[0.64,false],"passed":false},{"actual":[0.0,false],"check":"unequal design weights are respected","expected":[0.0,false],"passed":true},{"actual":[0.40016,false],"check":"ratio weights not in percent","expected":[0.4,false],"passed":false},{"actual":[16.025641,true],"check":"clear mismatch at alpha 0.001","expected":[16.0,true],"passed":false},{"actual":[4.001601,false],"check":"moderate imbalance below the strict threshold","expected":[4.0,false],"passed":false},{"actual":[37.652511,true],"check":"three arms uneven","expected":[38.0,true],"passed":false},{"actual":[0.263989,false],"check":"arm count sample 1","expected":[0.263918,false],"passed":false},{"actual":[437.727679,true],"check":"arm count sample 2","expected":[2400.801481,true],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"balanced split within noise\", \"actual\": [0.640041, false], \"expected\": [0.64, false], \"passed\": false}, {\"check\": \"unequal design weights are respected\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"ratio weights not in percent\", \"actual\": [0.40016, false], \"expected\": [0.4, false], \"passed\": false}, {\"check\": \"clear mismatch at alpha 0.001\", \"actual\": [16.025641, true], \"expected\": [16.0, true], \"passed\": false}, {\"check\": \"moderate imbalance below the strict threshold\", \"actual\": [4.001601, false], \"expected\": [4.0, false], \"passed\": false}, {\"check\": \"three arms uneven\", \"actual\": [37.652511, true], \"expected\": [38.0, true], \"passed\": false}, {\"check\": \"arm count sample 1\", \"actual\": [0.263989, false], \"expected\": [0.263918, false], \"passed\": false}, {\"check\": \"arm count sample 2\", \"actual\": [437.727679, true], \"expected\": [2400.801481, true], \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":96.31,"exit_code":0,"observations":[{"actual":[0.64,false],"check":"balanced split within noise","expected":[0.64,false],"passed":true},{"actual":[0.0,false],"check":"unequal design weights are respected","expected":[0.0,false],"passed":true},{"actual":[0.4,false],"check":"ratio weights not in percent","expected":[0.4,false],"passed":true},{"actual":[16.0,true],"check":"clear mismatch at alpha 0.001","expected":[16.0,true],"passed":true},{"actual":[4.0,false],"check":"moderate imbalance below the strict threshold","expected":[4.0,false],"passed":true},{"actual":[38.0,true],"check":"three arms uneven","expected":[38.0,true],"passed":true},{"actual":[0.263918,false],"check":"arm count sample 1","expected":[0.263918,false],"passed":true},{"actual":[2400.801481,true],"check":"arm count sample 2","expected":[2400.801481,true],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"balanced split within noise\", \"actual\": [0.64, false], \"expected\": [0.64, false], \"passed\": true}, {\"check\": \"unequal design weights are respected\", \"actual\": [0.0, false], \"expected\": [0.0, false], \"passed\": true}, {\"check\": \"ratio weights not in percent\", \"actual\": [0.4, false], \"expected\": [0.4, false], \"passed\": true}, {\"check\": \"clear mismatch at alpha 0.001\", \"actual\": [16.0, true], \"expected\": [16.0, true], \"passed\": true}, {\"check\": \"moderate imbalance below the strict threshold\", \"actual\": [4.0, false], \"expected\": [4.0, false], \"passed\": true}, {\"check\": \"three arms uneven\", \"actual\": [38.0, true], \"expected\": [38.0, true], \"passed\": true}, {\"check\": \"arm count sample 1\", \"actual\": [0.263918, false], \"expected\": [0.263918, false], \"passed\": true}, {\"check\": \"arm count sample 2\", \"actual\": [2400.801481, true], \"expected\": [2400.801481, true], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}