{"abstract":"A deliberate 2:1 allocation is flagged as a sample ratio mismatch on every readout.","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 100 assumes weights are percentages and breaks ratio-style weights.","family":"w2-experiment-statistics-srm-chi-square-weight-normalisation","id":"FA-74346","implementations":{"attempt":{"sha256":"5fb98384b06b30048ebba78dcf16ae94d92ec217fa402cf6cbdbc6bc4aa50eb9","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 / 100\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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 [('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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 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 8', [[0, 0, 152, 0], [0, 1, 50, 1]], [6.08, False]),\n  ('arm count sample 11', [[92, 0], [2, 1]], [46.0, True]),\n  ('arm count sample 14', [[4851, 168], [50, 1]], [50.190879, 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 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 16', [[0, 0, 44, 31], [2, 0, 3, 50]], [412.339111, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, 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 21', [[67, 446, 551], [1, 50, 50]], [316.144117, True]),\n  ('arm count sample 25', [[26, 4871], [1, 50]], [52.081033, True]),\n  ('arm count sample 32', [[490, 265, 153, 180], [3, 2, 1, 1]], [11.893229, False])]]\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":"f4cd41baf7378040f7a8fca41e958c34b288f928a1928b99fd05ecf36b49f8d5","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 / len(counts)\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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 [('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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 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 8', [[0, 0, 152, 0], [0, 1, 50, 1]], [6.08, False]),\n  ('arm count sample 11', [[92, 0], [2, 1]], [46.0, True]),\n  ('arm count sample 14', [[4851, 168], [50, 1]], [50.190879, 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 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 16', [[0, 0, 44, 31], [2, 0, 3, 50]], [412.339111, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, 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 21', [[67, 446, 551], [1, 50, 50]], [316.144117, True]),\n  ('arm count sample 25', [[26, 4871], [1, 50]], [52.081033, True]),\n  ('arm count sample 32', [[490, 265, 153, 180], [3, 2, 1, 1]], [11.893229, False])]]\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":"b82b4c515751496414a8d836693758d33016397f2a9e3bc729b1ac134be2585b","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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 [('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  ('empty zero-weight arm is ignored', [[520, 480, 0], [1, 1, 0]], [1.6, 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 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 8', [[0, 0, 152, 0], [0, 1, 50, 1]], [6.08, False]),\n  ('arm count sample 11', [[92, 0], [2, 1]], [46.0, True]),\n  ('arm count sample 14', [[4851, 168], [50, 1]], [50.190879, 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 15', [[72, 0, 108], [3, 0, 50]], [397.488, True]),\n  ('arm count sample 16', [[0, 0, 44, 31], [2, 0, 3, 50]], [412.339111, True]),\n  ('arm count sample 23', [[99, 96], [2, 3]], [9.423077, 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 21', [[67, 446, 551], [1, 50, 50]], [316.144117, True]),\n  ('arm count sample 25', [[26, 4871], [1, 50]], [52.081033, True]),\n  ('arm count sample 32', [[490, 265, 153, 180], [3, 2, 1, 1]], [11.893229, False])]]\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-weight-normalisation","generated_at":"2026-09-29T14:48:55.912283+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":"Compute expected counts from total * w / sum(weights).","root_cause":"Expected counts are total / number of arms, ignoring the configured weights.","sha256":"ff14b7286a75630721935b2d349eb6b4fb2ed7eb55242c8e4e74bd57cb10054c","title":"Sample ratio mismatch check: Design weights are assumed to be equal · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":62.573,"exit_code":1,"observations":[{"actual":[0.64,false],"check":"balanced split within noise","expected":[0.64,false],"passed":true},{"actual":[94090.0,true],"check":"unequal design weights are respected","expected":[0.0,false],"passed":false},{"actual":[48040.0,true],"check":"ratio weights not in percent","expected":[0.4,false],"passed":false},{"actual":[481000.0,true],"check":"clear mismatch at alpha 0.001","expected":[16.0,true],"passed":false},{"actual":[480400.0,true],"check":"moderate imbalance below the strict threshold","expected":[4.0,false],"passed":false},{"actual":[48100.0,true],"check":"empty zero-weight arm is ignored","expected":[1.6,false],"passed":false},{"actual":[314900.0,true],"check":"three arms uneven","expected":[38.0,true],"passed":false},{"actual":[14289.265292,true],"check":"arm count sample 1","expected":[0.263918,false],"passed":false}],"passed":false,"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\": [94090.0, true], \"expected\": [0.0, false], \"passed\": false}, {\"check\": \"ratio weights not in percent\", \"actual\": [48040.0, true], \"expected\": [0.4, false], \"passed\": false}, {\"check\": \"clear mismatch at alpha 0.001\", \"actual\": [481000.0, true], \"expected\": [16.0, true], \"passed\": false}, {\"check\": \"moderate imbalance below the strict threshold\", \"actual\": [480400.0, true], \"expected\": [4.0, false], \"passed\": false}, {\"check\": \"empty zero-weight arm is ignored\", \"actual\": [48100.0, true], \"expected\": [1.6, false], \"passed\": false}, {\"check\": \"three arms uneven\", \"actual\": [314900.0, true], \"expected\": [38.0, true], \"passed\": false}, {\"check\": \"arm count sample 1\", \"actual\": [14289.265292, true], \"expected\": [0.263918, false], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.343,"exit_code":1,"observations":[{"actual":[0.64,false],"check":"balanced split within noise","expected":[0.64,false],"passed":true},{"actual":[333.333333,true],"check":"unequal design weights are respected","expected":[0.0,false],"passed":false},{"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":[169.066667,true],"check":"empty zero-weight arm is ignored","expected":[1.6,false],"passed":false},{"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}],"passed":false,"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\": [333.333333, true], \"expected\": [0.0, false], \"passed\": false}, {\"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\": \"empty zero-weight arm is ignored\", \"actual\": [169.066667, true], \"expected\": [1.6, false], \"passed\": false}, {\"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}], \"passed\": false}\n"},"fixed":{"elapsed_ms":50.18,"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":[1.6,false],"check":"empty zero-weight arm is ignored","expected":[1.6,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}],"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\": \"empty zero-weight arm is ignored\", \"actual\": [1.6, false], \"expected\": [1.6, 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}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}