{"abstract":"High implied volatilities are reported as 1.0.","category":"Options payoff and settlement","checks":8,"contract":"Inputs kind, option price, S, K, r, T (no dividends). If price <= lower bound (call max(S - K e^{-rT},0), put max(K e^{-rT} - S,0)) or price >= upper bound (call S, put K e^{-rT}) return None. Otherwise bisect sigma in [1e-6, 5] for 100 iterations on the Black-Scholes price and return the midpoint rounded to 4.","evaluation_group":"w2-options_payoff_and_settlement-implied-volatility-bisection","failed_approach":"Doubling the bracket to 2.0 still truncates very high volatilities.","family":"w2-options_payoff_and_settlement-implied-volatility-bisection-volatility-search-bracket","id":"FA-61741","implementations":{"attempt":{"sha256":"7f5fb5fd24e44b96fa709aae33402ec8782d4d273d73cf9de710dabb6a95e26b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, price, S, K, r, T):\n    def N(x):\n        return 0.5 * (1 + math.erf(x / math.sqrt(2)))\n    def bs(v):\n        d1 = (math.log(S / K) + (r + 0.5 * v * v) * T) / (v * math.sqrt(T))\n        d2 = d1 - v * math.sqrt(T)\n        if kind == 'C':\n            return S * N(d1) - K * math.exp(-r * T) * N(d2)\n        return K * math.exp(-r * T) * N(-d2) - S * N(-d1)\n    disc_k = K * math.exp(-r * T)\n    lower = max(S - disc_k, 0) if kind == 'C' else max(disc_k - S, 0)\n    upper = S if kind == 'C' else disc_k\n    if price <= lower or price >= upper:\n        return None\n    lo, hi = 1e-6, 2.0\n    for _ in range(100):\n        mid = (lo + hi) / 2\n        if bs(mid) > price:\n            hi = mid\n        else:\n            lo = mid\n    return round((lo + hi) / 2, 4)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression volatility search bracket 1', ['C', 91.2319, 100.0, 120.0, 0.0, 1.0], 3.5], ['regression volatility search bracket 2', ['P', 91.5993, 110.0, 100.0, 0.0, 1.0], 3.5], ['partial repair probe 1', ['P', 94.176453, 110.0, 100.0, 0.03, 2.0], 5.0], ['partial repair probe 2', ['P', 97.044553, 100.0, 100.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 1.2175, 110.0, 120.0, 0.0, 1.0], 0.1], ['normal control 2', ['P', 3.8044, 110.0, 95.0, 0.08, 1.0], 0.3], ['normal control 3', ['P', 5.59, 100.0, 100.0, 0.03, 0.25], 0.3], ['normal control 4', ['P', 2.5, 90.0, 95.0, 0.08, 2.0], 0.1239]], [['regression volatility search bracket 1', ['C', 92.3037, 100.0, 100.0, 0.08, 1.0], 3.5], ['regression volatility search bracket 2', ['C', 56.0677, 90.0, 100.0, 0.0, 2.0], 1.3], ['partial repair probe 1', ['C', 79.4077, 100.0, 95.0, 0.0, 1.0], 2.5], ['partial repair probe 2', ['C', 70.1631, 110.0, 100.0, 0.03, 0.25], 3.5], ['normal control 1', ['P', 9.2391, 100.0, 100.0, 0.08, 2.0], 0.3], ['normal control 2', ['P', 3.6659, 100.0, 95.0, 0.0, 0.25], 0.3], ['normal control 3', ['P', 92.311635, 100.0, 100.0, 0.08, 1.0], None], ['normal control 4', ['P', 92.192326, 100.0, 95.0, 0.03, 1.0], None]], [['regression volatility search bracket 1', ['C', 87.8484, 110.0, 100.0, 0.0, 1.0], 2.5], ['regression volatility search bracket 2', ['C', 56.8158, 110.0, 100.0, 0.03, 1.0], 1.3], ['partial repair probe 1', ['C', 101.9338, 110.0, 100.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 116.453464, 110.0, 120.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 0.4365, 90.0, 95.0, 0.03, 0.25], 0.1], ['normal control 2', ['P', 0.0, 110.0, 95.0, 0.0, 2.0], None], ['normal control 3', ['P', 10.0, 90.0, 100.0, 0.0, 0.25], None], ['normal control 4', ['C', 8.8425, 100.0, 100.0, 0.08, 1.0], 0.1]], [['regression volatility search bracket 1', ['P', 77.5605, 100.0, 120.0, 0.03, 0.25], 3.5], ['regression volatility search bracket 2', ['C', 58.7023, 100.0, 120.0, 0.08, 0.25], 3.5], ['partial repair probe 1', ['C', 102.1427, 110.0, 95.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 92.29, 100.0, 100.0, 0.0, 2.0], 2.5], ['normal control 1', ['C', 0.01, 100.0, 100.0, 0.08, 2.0], None], ['normal control 2', ['P', 36.4553, 90.0, 120.0, 0.0, 2.0], 0.3], ['normal control 3', ['C', 0.01, 90.0, 120.0, 0.08, 0.25], 0.1967], ['normal control 4', ['C', 0.01, 90.0, 120.0, 0.03, 1.0], 0.0951]], [['regression volatility search bracket 1', ['C', 55.7612, 110.0, 95.0, 0.0, 0.25], 2.5], ['regression volatility search bracket 2', ['C', 70.09, 110.0, 120.0, 0.03, 2.0], 1.3], ['partial repair probe 1', ['C', 54.2681, 110.0, 100.0, 0.0, 0.25], 2.5], ['partial repair probe 2', ['P', 88.7705, 110.0, 100.0, 0.03, 1.0], 3.5], ['normal control 1', ['C', 12.9118, 110.0, 100.0, 0.03, 2.0], None], ['normal control 2', ['C', 110.0, 110.0, 95.0, 0.08, 0.25], None], ['normal control 3', ['P', 87.696053, 100.0, 95.0, 0.08, 1.0], None], ['normal control 4', ['P', 19.99, 100.0, 120.0, 0.0, 2.0], None]]]\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":"b31159e23f35103743cec8ad0585ca85ea8a21617f73f3e3a1791e02f99efd96","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, price, S, K, r, T):\n    def N(x):\n        return 0.5 * (1 + math.erf(x / math.sqrt(2)))\n    def bs(v):\n        d1 = (math.log(S / K) + (r + 0.5 * v * v) * T) / (v * math.sqrt(T))\n        d2 = d1 - v * math.sqrt(T)\n        if kind == 'C':\n            return S * N(d1) - K * math.exp(-r * T) * N(d2)\n        return K * math.exp(-r * T) * N(-d2) - S * N(-d1)\n    disc_k = K * math.exp(-r * T)\n    lower = max(S - disc_k, 0) if kind == 'C' else max(disc_k - S, 0)\n    upper = S if kind == 'C' else disc_k\n    if price <= lower or price >= upper:\n        return None\n    lo, hi = 1e-6, 1.0\n    for _ in range(100):\n        mid = (lo + hi) / 2\n        if bs(mid) > price:\n            hi = mid\n        else:\n            lo = mid\n    return round((lo + hi) / 2, 4)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression volatility search bracket 1', ['C', 91.2319, 100.0, 120.0, 0.0, 1.0], 3.5], ['regression volatility search bracket 2', ['P', 91.5993, 110.0, 100.0, 0.0, 1.0], 3.5], ['partial repair probe 1', ['P', 94.176453, 110.0, 100.0, 0.03, 2.0], 5.0], ['partial repair probe 2', ['P', 97.044553, 100.0, 100.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 1.2175, 110.0, 120.0, 0.0, 1.0], 0.1], ['normal control 2', ['P', 3.8044, 110.0, 95.0, 0.08, 1.0], 0.3], ['normal control 3', ['P', 5.59, 100.0, 100.0, 0.03, 0.25], 0.3], ['normal control 4', ['P', 2.5, 90.0, 95.0, 0.08, 2.0], 0.1239]], [['regression volatility search bracket 1', ['C', 92.3037, 100.0, 100.0, 0.08, 1.0], 3.5], ['regression volatility search bracket 2', ['C', 56.0677, 90.0, 100.0, 0.0, 2.0], 1.3], ['partial repair probe 1', ['C', 79.4077, 100.0, 95.0, 0.0, 1.0], 2.5], ['partial repair probe 2', ['C', 70.1631, 110.0, 100.0, 0.03, 0.25], 3.5], ['normal control 1', ['P', 9.2391, 100.0, 100.0, 0.08, 2.0], 0.3], ['normal control 2', ['P', 3.6659, 100.0, 95.0, 0.0, 0.25], 0.3], ['normal control 3', ['P', 92.311635, 100.0, 100.0, 0.08, 1.0], None], ['normal control 4', ['P', 92.192326, 100.0, 95.0, 0.03, 1.0], None]], [['regression volatility search bracket 1', ['C', 87.8484, 110.0, 100.0, 0.0, 1.0], 2.5], ['regression volatility search bracket 2', ['C', 56.8158, 110.0, 100.0, 0.03, 1.0], 1.3], ['partial repair probe 1', ['C', 101.9338, 110.0, 100.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 116.453464, 110.0, 120.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 0.4365, 90.0, 95.0, 0.03, 0.25], 0.1], ['normal control 2', ['P', 0.0, 110.0, 95.0, 0.0, 2.0], None], ['normal control 3', ['P', 10.0, 90.0, 100.0, 0.0, 0.25], None], ['normal control 4', ['C', 8.8425, 100.0, 100.0, 0.08, 1.0], 0.1]], [['regression volatility search bracket 1', ['P', 77.5605, 100.0, 120.0, 0.03, 0.25], 3.5], ['regression volatility search bracket 2', ['C', 58.7023, 100.0, 120.0, 0.08, 0.25], 3.5], ['partial repair probe 1', ['C', 102.1427, 110.0, 95.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 92.29, 100.0, 100.0, 0.0, 2.0], 2.5], ['normal control 1', ['C', 0.01, 100.0, 100.0, 0.08, 2.0], None], ['normal control 2', ['P', 36.4553, 90.0, 120.0, 0.0, 2.0], 0.3], ['normal control 3', ['C', 0.01, 90.0, 120.0, 0.08, 0.25], 0.1967], ['normal control 4', ['C', 0.01, 90.0, 120.0, 0.03, 1.0], 0.0951]], [['regression volatility search bracket 1', ['C', 55.7612, 110.0, 95.0, 0.0, 0.25], 2.5], ['regression volatility search bracket 2', ['C', 70.09, 110.0, 120.0, 0.03, 2.0], 1.3], ['partial repair probe 1', ['C', 54.2681, 110.0, 100.0, 0.0, 0.25], 2.5], ['partial repair probe 2', ['P', 88.7705, 110.0, 100.0, 0.03, 1.0], 3.5], ['normal control 1', ['C', 12.9118, 110.0, 100.0, 0.03, 2.0], None], ['normal control 2', ['C', 110.0, 110.0, 95.0, 0.08, 0.25], None], ['normal control 3', ['P', 87.696053, 100.0, 95.0, 0.08, 1.0], None], ['normal control 4', ['P', 19.99, 100.0, 120.0, 0.0, 2.0], None]]]\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":"59dfc3e9603224462fed12aa568e07d8d391753c2d9d24153567720d98806550","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, price, S, K, r, T):\n    def N(x):\n        return 0.5 * (1 + math.erf(x / math.sqrt(2)))\n    def bs(v):\n        d1 = (math.log(S / K) + (r + 0.5 * v * v) * T) / (v * math.sqrt(T))\n        d2 = d1 - v * math.sqrt(T)\n        if kind == 'C':\n            return S * N(d1) - K * math.exp(-r * T) * N(d2)\n        return K * math.exp(-r * T) * N(-d2) - S * N(-d1)\n    disc_k = K * math.exp(-r * T)\n    lower = max(S - disc_k, 0) if kind == 'C' else max(disc_k - S, 0)\n    upper = S if kind == 'C' else disc_k\n    if price <= lower or price >= upper:\n        return None\n    lo, hi = 1e-6, 5.0\n    for _ in range(100):\n        mid = (lo + hi) / 2\n        if bs(mid) > price:\n            hi = mid\n        else:\n            lo = mid\n    return round((lo + hi) / 2, 4)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression volatility search bracket 1', ['C', 91.2319, 100.0, 120.0, 0.0, 1.0], 3.5], ['regression volatility search bracket 2', ['P', 91.5993, 110.0, 100.0, 0.0, 1.0], 3.5], ['partial repair probe 1', ['P', 94.176453, 110.0, 100.0, 0.03, 2.0], 5.0], ['partial repair probe 2', ['P', 97.044553, 100.0, 100.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 1.2175, 110.0, 120.0, 0.0, 1.0], 0.1], ['normal control 2', ['P', 3.8044, 110.0, 95.0, 0.08, 1.0], 0.3], ['normal control 3', ['P', 5.59, 100.0, 100.0, 0.03, 0.25], 0.3], ['normal control 4', ['P', 2.5, 90.0, 95.0, 0.08, 2.0], 0.1239]], [['regression volatility search bracket 1', ['C', 92.3037, 100.0, 100.0, 0.08, 1.0], 3.5], ['regression volatility search bracket 2', ['C', 56.0677, 90.0, 100.0, 0.0, 2.0], 1.3], ['partial repair probe 1', ['C', 79.4077, 100.0, 95.0, 0.0, 1.0], 2.5], ['partial repair probe 2', ['C', 70.1631, 110.0, 100.0, 0.03, 0.25], 3.5], ['normal control 1', ['P', 9.2391, 100.0, 100.0, 0.08, 2.0], 0.3], ['normal control 2', ['P', 3.6659, 100.0, 95.0, 0.0, 0.25], 0.3], ['normal control 3', ['P', 92.311635, 100.0, 100.0, 0.08, 1.0], None], ['normal control 4', ['P', 92.192326, 100.0, 95.0, 0.03, 1.0], None]], [['regression volatility search bracket 1', ['C', 87.8484, 110.0, 100.0, 0.0, 1.0], 2.5], ['regression volatility search bracket 2', ['C', 56.8158, 110.0, 100.0, 0.03, 1.0], 1.3], ['partial repair probe 1', ['C', 101.9338, 110.0, 100.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 116.453464, 110.0, 120.0, 0.03, 1.0], 5.0], ['normal control 1', ['C', 0.4365, 90.0, 95.0, 0.03, 0.25], 0.1], ['normal control 2', ['P', 0.0, 110.0, 95.0, 0.0, 2.0], None], ['normal control 3', ['P', 10.0, 90.0, 100.0, 0.0, 0.25], None], ['normal control 4', ['C', 8.8425, 100.0, 100.0, 0.08, 1.0], 0.1]], [['regression volatility search bracket 1', ['P', 77.5605, 100.0, 120.0, 0.03, 0.25], 3.5], ['regression volatility search bracket 2', ['C', 58.7023, 100.0, 120.0, 0.08, 0.25], 3.5], ['partial repair probe 1', ['C', 102.1427, 110.0, 95.0, 0.08, 1.0], 3.5], ['partial repair probe 2', ['P', 92.29, 100.0, 100.0, 0.0, 2.0], 2.5], ['normal control 1', ['C', 0.01, 100.0, 100.0, 0.08, 2.0], None], ['normal control 2', ['P', 36.4553, 90.0, 120.0, 0.0, 2.0], 0.3], ['normal control 3', ['C', 0.01, 90.0, 120.0, 0.08, 0.25], 0.1967], ['normal control 4', ['C', 0.01, 90.0, 120.0, 0.03, 1.0], 0.0951]], [['regression volatility search bracket 1', ['C', 55.7612, 110.0, 95.0, 0.0, 0.25], 2.5], ['regression volatility search bracket 2', ['C', 70.09, 110.0, 120.0, 0.03, 2.0], 1.3], ['partial repair probe 1', ['C', 54.2681, 110.0, 100.0, 0.0, 0.25], 2.5], ['partial repair probe 2', ['P', 88.7705, 110.0, 100.0, 0.03, 1.0], 3.5], ['normal control 1', ['C', 12.9118, 110.0, 100.0, 0.03, 2.0], None], ['normal control 2', ['C', 110.0, 110.0, 95.0, 0.08, 0.25], None], ['normal control 3', ['P', 87.696053, 100.0, 95.0, 0.08, 1.0], None], ['normal control 4', ['P', 19.99, 100.0, 120.0, 0.0, 2.0], None]]]\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 contract stated explicitly in the contract field; no claim of conformance to any exchange or clearing rulebook. 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-options_payoff_and_settlement-implied-volatility-bisection-volatility-search-bracket","generated_at":"2026-09-29T14:46:58.092734+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.","repair":"Search up to 5.0 as the contract states.","root_cause":"The upper end of the bisection bracket is 1.0.","sha256":"fd4926193f68dd378bb541ad0aa1357c4a18b00fb7191f3f9b99662e8ca5d83f","title":"Implied volatility by bisection with arbitrage bounds: the search bracket tops out at 100% volatility · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":40.815,"exit_code":1,"observations":[{"actual":2.0,"check":"regression volatility search bracket 1","expected":3.5,"passed":false},{"actual":2.0,"check":"regression volatility search bracket 2","expected":3.5,"passed":false},{"actual":2.0,"check":"partial repair probe 1","expected":5.0,"passed":false},{"actual":2.0,"check":"partial repair probe 2","expected":5.0,"passed":false},{"actual":0.1,"check":"normal control 1","expected":0.1,"passed":true},{"actual":0.3,"check":"normal control 2","expected":0.3,"passed":true},{"actual":0.3,"check":"normal control 3","expected":0.3,"passed":true},{"actual":0.1239,"check":"normal control 4","expected":0.1239,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression volatility search bracket 1\", \"actual\": 2.0, \"expected\": 3.5, \"passed\": false}, {\"check\": \"regression volatility search bracket 2\", \"actual\": 2.0, \"expected\": 3.5, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 2.0, \"expected\": 5.0, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 2.0, \"expected\": 5.0, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 0.1, \"expected\": 0.1, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0.1239, \"expected\": 0.1239, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":42.15,"exit_code":1,"observations":[{"actual":1.0,"check":"regression volatility search bracket 1","expected":3.5,"passed":false},{"actual":1.0,"check":"regression volatility search bracket 2","expected":3.5,"passed":false},{"actual":1.0,"check":"partial repair probe 1","expected":5.0,"passed":false},{"actual":1.0,"check":"partial repair probe 2","expected":5.0,"passed":false},{"actual":0.1,"check":"normal control 1","expected":0.1,"passed":true},{"actual":0.3,"check":"normal control 2","expected":0.3,"passed":true},{"actual":0.3,"check":"normal control 3","expected":0.3,"passed":true},{"actual":0.1239,"check":"normal control 4","expected":0.1239,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression volatility search bracket 1\", \"actual\": 1.0, \"expected\": 3.5, \"passed\": false}, {\"check\": \"regression volatility search bracket 2\", \"actual\": 1.0, \"expected\": 3.5, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 1.0, \"expected\": 5.0, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 1.0, \"expected\": 5.0, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 0.1, \"expected\": 0.1, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0.1239, \"expected\": 0.1239, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":38.129,"exit_code":0,"observations":[{"actual":3.5,"check":"regression volatility search bracket 1","expected":3.5,"passed":true},{"actual":3.5,"check":"regression volatility search bracket 2","expected":3.5,"passed":true},{"actual":5.0,"check":"partial repair probe 1","expected":5.0,"passed":true},{"actual":5.0,"check":"partial repair probe 2","expected":5.0,"passed":true},{"actual":0.1,"check":"normal control 1","expected":0.1,"passed":true},{"actual":0.3,"check":"normal control 2","expected":0.3,"passed":true},{"actual":0.3,"check":"normal control 3","expected":0.3,"passed":true},{"actual":0.1239,"check":"normal control 4","expected":0.1239,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression volatility search bracket 1\", \"actual\": 3.5, \"expected\": 3.5, \"passed\": true}, {\"check\": \"regression volatility search bracket 2\", \"actual\": 3.5, \"expected\": 3.5, \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": 5.0, \"expected\": 5.0, \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": 5.0, \"expected\": 5.0, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0.1, \"expected\": 0.1, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 0.3, \"expected\": 0.3, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0.1239, \"expected\": 0.1239, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}