{"abstract":"Two entries tied for first each receive the first-place prize.","category":"Fantasy sports scoring","checks":7,"contract":"Daily contest entries are [entry id, score in hundredths]; payouts[r] is the prize in cents for finishing position r (0-based), positions past the list pay 0. Entries with equal scores share the positions they jointly occupy: their prizes are summed and split equally, whole cents only, with leftover cents given one each to the tied entries in ascending id order. Return id -> cents for entries receiving more than 0.","contract_signature":"entries, payouts","evaluation_group":"w2-fantasy-sports-scoring-contest-payout-ties","failed_approach":"Extending the pool one position too far also takes the next finisher's prize.","family":"w2-fantasy-sports-scoring-contest-payout-ties-tied-position-pool","id":"FA-85031","implementations":{"attempt":{"sha256":"81bfdcda994c0c6ac0c2f6b0d427de3d698b84f8563b1668b5c8c053fc2392e9","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries, payouts):\n    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))\n    out = {}\n    i = 0\n    while i < len(ordered):\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        pool = sum(payouts[i:j + 1])\n        group = sorted(e[0] for e in ordered[i:j])\n        share, extra = divmod(pool, len(group))\n        for k, eid in enumerate(group):\n            amt = share + (1 if k < extra else 0)\n            if amt > 0:\n                out[eid] = amt\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tied position pool',\n   [[['e09', 1365], ['e21', 1420], ['e28', 1415], ['e20', 1417], ['e06', 1420], ['e25', 1410], ['e23', 1413]],\n    [199, 77]],\n   {'e06': 138, 'e21': 138}),\n  ('partial repair probe: tied position pool',\n   [[['e15', 1500], ['e29', 1493], ['e07', 1490], ['e13', 1495]], [1000, 100]], {'e13': 100, 'e15': 1000}),\n  ('second regression', [[['e17', 1328], ['e09', 1333], ['e28', 1326], ['e11', 1333]], [301, 1]],\n   {'e09': 151, 'e11': 151}),\n  ('normal control 1',\n   [[['e16', 1278], ['e04', 1278], ['e26', 1326], ['e21', 1333], ['e10', 1328], ['e07', 1333]], [77, 77]],\n   {'e07': 77, 'e21': 77}),\n  ('normal control 2', [[['e20', 1500], ['e12', 1445], ['e23', 1500]], [501, 501]], {'e20': 501, 'e23': 501}),\n  ('normal control 3',\n   [[['e26', 1365], ['e06', 1413], ['e23', 1413], ['e12', 1415], ['e24', 1420], ['e27', 1417], ['e17', 1420],\n     ['e01', 1415], ['e29', 1410]],\n    [301, 301]],\n   {'e17': 301, 'e24': 301}),\n  ('normal control 4',\n   [[['e24', 1495], ['e20', 1445], ['e28', 1445], ['e12', 1445], ['e27', 1500], ['e11', 1500], ['e10', 1445],\n     ['e18', 1500]],\n    [500, 500, 500]],\n   {'e11': 500, 'e18': 500, 'e27': 500})],\n [('regression: tied position pool',\n   [[['e29', 1415], ['e18', 1415], ['e01', 1417], ['e05', 1410]], [301, 250]],\n   {'e01': 301, 'e18': 125, 'e29': 125}),\n  ('partial repair probe: tied position pool',\n   [[['e20', 1420], ['e09', 1417], ['e07', 1365]], [501, 199, 199, 1]],\n   {'e07': 199, 'e09': 199, 'e20': 501}),\n  ('second regression',\n   [[['e27', 1500], ['e15', 1493], ['e12', 1445], ['e24', 1445], ['e19', 1493], ['e29', 1445], ['e05', 1493],\n     ['e13', 1445], ['e04', 1493]],\n    [501, 301, 301, 199, 2]],\n   {'e04': 201, 'e05': 201, 'e15': 201, 'e19': 200, 'e27': 501}),\n  ('normal control 1',\n   [[['e17', 1410], ['e19', 1365], ['e25', 1365], ['e12', 1420], ['e20', 1413], ['e27', 1417], ['e28', 1365],\n     ['e10', 1420], ['e08', 1365]],\n    [1, 1]],\n   {'e10': 1, 'e12': 1}),\n  ('normal control 2',\n   [[['e24', 1415], ['e15', 1413], ['e13', 1415], ['e03', 1410], ['e22', 1420], ['e02', 1420], ['e01', 1415]],\n    [199, 199]],\n   {'e02': 199, 'e22': 199}),\n  ('normal control 3',\n   [[['e28', 1333], ['e20', 1326], ['e01', 1278], ['e19', 1278], ['e04', 1330], ['e09', 1323], ['e02', 1333],\n     ['e03', 1326]],\n    [250, 250]],\n   {'e02': 250, 'e28': 250})],\n [('regression: tied position pool',\n   [[['e02', 1420], ['e15', 1417], ['e11', 1420], ['e24', 1365], ['e16', 1365], ['e06', 1415], ['e23', 1415],\n     ['e14', 1365], ['e12', 1420]],\n    [77, 77, 1]],\n   {'e02': 52, 'e11': 52, 'e12': 51}),\n  ('partial repair probe: tied position pool',\n   [[['e20', 1490], ['e18', 1500], ['e21', 1497], ['e23', 1495], ['e09', 1493], ['e29', 1493]], [1000, 199]],\n   {'e18': 1000, 'e21': 199}),\n  ('second regression',\n   [[['e16', 1493], ['e07', 1500], ['e13', 1500], ['e23', 1500], ['e20', 1500], ['e10', 1497], ['e09', 1500],\n     ['e27', 1495]],\n    [501, 250, 250, 2]],\n   {'e07': 201, 'e09': 201, 'e13': 201, 'e20': 200, 'e23': 200}),\n  ('normal control 1',\n   [[['e26', 1413], ['e13', 1415], ['e24', 1420], ['e12', 1365], ['e09', 1415], ['e23', 1410], ['e07', 1413],\n     ['e29', 1420], ['e17', 1417]],\n    [1, 1]],\n   {'e24': 1, 'e29': 1}),\n  ('normal control 2',\n   [[['e19', 1420], ['e03', 1413], ['e23', 1410], ['e13', 1420], ['e15', 1410]], [301, 301]],\n   {'e13': 301, 'e19': 301}),\n  ('normal control 3',\n   [[['e06', 1415], ['e02', 1420], ['e13', 1410], ['e14', 1417], ['e12', 1417], ['e18', 1365], ['e15', 1420]],\n    [250, 250]],\n   {'e02': 250, 'e15': 250}),\n  ('normal control 4',\n   [[['e20', 1500], ['e21', 1495], ['e01', 1445], ['e16', 1495], ['e27', 1445], ['e11', 1493], ['e24', 1500],\n     ['e13', 1497]],\n    [100, 100]],\n   {'e20': 100, 'e24': 100})],\n [('regression: tied position pool',\n   [[['e19', 1420], ['e09', 1417], ['e26', 1365], ['e13', 1410], ['e15', 1365], ['e21', 1415], ['e22', 1410],\n     ['e25', 1365], ['e18', 1365]],\n    [501, 250, 250, 100, 2]],\n   {'e09': 250, 'e13': 51, 'e19': 501, 'e21': 250, 'e22': 51}),\n  ('partial repair probe: tied position pool',\n   [[['e08', 1326], ['e11', 1323], ['e10', 1328], ['e09', 1326], ['e05', 1330], ['e29', 1278], ['e26', 1323]],\n    [500, 199]],\n   {'e05': 500, 'e10': 199}),\n  ('second regression',\n   [[['e16', 1328], ['e10', 1278], ['e14', 1333], ['e23', 1278], ['e22', 1333], ['e03', 1326], ['e07', 1333],\n     ['e28', 1323]],\n    [500, 2]],\n   {'e07': 168, 'e14': 167, 'e22': 167}),\n  ('normal control 1', [[['e14', 1420], ['e05', 1420], ['e03', 1415], ['e12', 1415]], [2, 2]],\n   {'e05': 2, 'e14': 2}),\n  ('normal control 2',\n   [[['e12', 1417], ['e06', 1420], ['e29', 1410], ['e28', 1420], ['e18', 1417], ['e11', 1410]], [199, 199]],\n   {'e06': 199, 'e28': 199}),\n  ('normal control 3', [[['e07', 1328], ['e02', 1333], ['e23', 1278], ['e05', 1333]], [199, 199]],\n   {'e02': 199, 'e05': 199}),\n  ('normal control 4',\n   [[['e07', 1495], ['e17', 1445], ['e22', 1493], ['e29', 1500], ['e09', 1490], ['e20', 1490], ['e15', 1500]],\n    [501, 501]],\n   {'e15': 501, 'e29': 501})],\n [('regression: tied position pool',\n   [[['e18', 1333], ['e22', 1326], ['e11', 1326], ['e16', 1333], ['e28', 1323], ['e13', 1278], ['e07', 1323],\n     ['e26', 1330]],\n    [301, 199, 2]],\n   {'e16': 250, 'e18': 250, 'e26': 2}),\n  ('partial repair probe: tied position pool',\n   [[['e02', 1333], ['e19', 1333], ['e08', 1326], ['e09', 1326], ['e25', 1328], ['e17', 1330], ['e16', 1333]],\n    [301, 100, 100, 2]],\n   {'e02': 167, 'e16': 167, 'e17': 2, 'e19': 167}),\n  ('second regression',\n   [[['e26', 1413], ['e12', 1415], ['e08', 1420], ['e20', 1420], ['e11', 1420], ['e28', 1365], ['e23', 1413]],\n    [250, 250, 199]],\n   {'e08': 233, 'e11': 233, 'e20': 233}),\n  ('normal control 1',\n   [[['e14', 1500], ['e27', 1497], ['e13', 1495], ['e03', 1493], ['e02', 1500]], [77, 77]],\n   {'e02': 77, 'e14': 77}),\n  ('normal control 2',\n   [[['e03', 1415], ['e21', 1420], ['e25', 1410], ['e13', 1420], ['e24', 1413], ['e26', 1415], ['e01', 1410]],\n    [301, 301]],\n   {'e13': 301, 'e21': 301}),\n  ('normal control 3',\n   [[['e28', 1420], ['e02', 1417], ['e23', 1420], ['e26', 1417], ['e16', 1417], ['e18', 1417]], [301, 301]],\n   {'e23': 301, 'e28': 301}),\n  ('normal control 4',\n   [[['e02', 1490], ['e09', 1500], ['e28', 1500], ['e29', 1497], ['e12', 1445], ['e19', 1490]], [301, 301]],\n   {'e09': 301, 'e28': 301})]]\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":"088d46f3421c36d01d97631c930f95e74e4d5fa36d59480fec2a33bb24fdf6ad","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries, payouts):\n    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))\n    out = {}\n    i = 0\n    while i < len(ordered):\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        pool = sum(payouts[i:i + 1]) * (j - i)\n        group = sorted(e[0] for e in ordered[i:j])\n        share, extra = divmod(pool, len(group))\n        for k, eid in enumerate(group):\n            amt = share + (1 if k < extra else 0)\n            if amt > 0:\n                out[eid] = amt\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tied position pool',\n   [[['e09', 1365], ['e21', 1420], ['e28', 1415], ['e20', 1417], ['e06', 1420], ['e25', 1410], ['e23', 1413]],\n    [199, 77]],\n   {'e06': 138, 'e21': 138}),\n  ('partial repair probe: tied position pool',\n   [[['e15', 1500], ['e29', 1493], ['e07', 1490], ['e13', 1495]], [1000, 100]], {'e13': 100, 'e15': 1000}),\n  ('second regression', [[['e17', 1328], ['e09', 1333], ['e28', 1326], ['e11', 1333]], [301, 1]],\n   {'e09': 151, 'e11': 151}),\n  ('normal control 1',\n   [[['e16', 1278], ['e04', 1278], ['e26', 1326], ['e21', 1333], ['e10', 1328], ['e07', 1333]], [77, 77]],\n   {'e07': 77, 'e21': 77}),\n  ('normal control 2', [[['e20', 1500], ['e12', 1445], ['e23', 1500]], [501, 501]], {'e20': 501, 'e23': 501}),\n  ('normal control 3',\n   [[['e26', 1365], ['e06', 1413], ['e23', 1413], ['e12', 1415], ['e24', 1420], ['e27', 1417], ['e17', 1420],\n     ['e01', 1415], ['e29', 1410]],\n    [301, 301]],\n   {'e17': 301, 'e24': 301}),\n  ('normal control 4',\n   [[['e24', 1495], ['e20', 1445], ['e28', 1445], ['e12', 1445], ['e27', 1500], ['e11', 1500], ['e10', 1445],\n     ['e18', 1500]],\n    [500, 500, 500]],\n   {'e11': 500, 'e18': 500, 'e27': 500})],\n [('regression: tied position pool',\n   [[['e29', 1415], ['e18', 1415], ['e01', 1417], ['e05', 1410]], [301, 250]],\n   {'e01': 301, 'e18': 125, 'e29': 125}),\n  ('partial repair probe: tied position pool',\n   [[['e20', 1420], ['e09', 1417], ['e07', 1365]], [501, 199, 199, 1]],\n   {'e07': 199, 'e09': 199, 'e20': 501}),\n  ('second regression',\n   [[['e27', 1500], ['e15', 1493], ['e12', 1445], ['e24', 1445], ['e19', 1493], ['e29', 1445], ['e05', 1493],\n     ['e13', 1445], ['e04', 1493]],\n    [501, 301, 301, 199, 2]],\n   {'e04': 201, 'e05': 201, 'e15': 201, 'e19': 200, 'e27': 501}),\n  ('normal control 1',\n   [[['e17', 1410], ['e19', 1365], ['e25', 1365], ['e12', 1420], ['e20', 1413], ['e27', 1417], ['e28', 1365],\n     ['e10', 1420], ['e08', 1365]],\n    [1, 1]],\n   {'e10': 1, 'e12': 1}),\n  ('normal control 2',\n   [[['e24', 1415], ['e15', 1413], ['e13', 1415], ['e03', 1410], ['e22', 1420], ['e02', 1420], ['e01', 1415]],\n    [199, 199]],\n   {'e02': 199, 'e22': 199}),\n  ('normal control 3',\n   [[['e28', 1333], ['e20', 1326], ['e01', 1278], ['e19', 1278], ['e04', 1330], ['e09', 1323], ['e02', 1333],\n     ['e03', 1326]],\n    [250, 250]],\n   {'e02': 250, 'e28': 250})],\n [('regression: tied position pool',\n   [[['e02', 1420], ['e15', 1417], ['e11', 1420], ['e24', 1365], ['e16', 1365], ['e06', 1415], ['e23', 1415],\n     ['e14', 1365], ['e12', 1420]],\n    [77, 77, 1]],\n   {'e02': 52, 'e11': 52, 'e12': 51}),\n  ('partial repair probe: tied position pool',\n   [[['e20', 1490], ['e18', 1500], ['e21', 1497], ['e23', 1495], ['e09', 1493], ['e29', 1493]], [1000, 199]],\n   {'e18': 1000, 'e21': 199}),\n  ('second regression',\n   [[['e16', 1493], ['e07', 1500], ['e13', 1500], ['e23', 1500], ['e20', 1500], ['e10', 1497], ['e09', 1500],\n     ['e27', 1495]],\n    [501, 250, 250, 2]],\n   {'e07': 201, 'e09': 201, 'e13': 201, 'e20': 200, 'e23': 200}),\n  ('normal control 1',\n   [[['e26', 1413], ['e13', 1415], ['e24', 1420], ['e12', 1365], ['e09', 1415], ['e23', 1410], ['e07', 1413],\n     ['e29', 1420], ['e17', 1417]],\n    [1, 1]],\n   {'e24': 1, 'e29': 1}),\n  ('normal control 2',\n   [[['e19', 1420], ['e03', 1413], ['e23', 1410], ['e13', 1420], ['e15', 1410]], [301, 301]],\n   {'e13': 301, 'e19': 301}),\n  ('normal control 3',\n   [[['e06', 1415], ['e02', 1420], ['e13', 1410], ['e14', 1417], ['e12', 1417], ['e18', 1365], ['e15', 1420]],\n    [250, 250]],\n   {'e02': 250, 'e15': 250}),\n  ('normal control 4',\n   [[['e20', 1500], ['e21', 1495], ['e01', 1445], ['e16', 1495], ['e27', 1445], ['e11', 1493], ['e24', 1500],\n     ['e13', 1497]],\n    [100, 100]],\n   {'e20': 100, 'e24': 100})],\n [('regression: tied position pool',\n   [[['e19', 1420], ['e09', 1417], ['e26', 1365], ['e13', 1410], ['e15', 1365], ['e21', 1415], ['e22', 1410],\n     ['e25', 1365], ['e18', 1365]],\n    [501, 250, 250, 100, 2]],\n   {'e09': 250, 'e13': 51, 'e19': 501, 'e21': 250, 'e22': 51}),\n  ('partial repair probe: tied position pool',\n   [[['e08', 1326], ['e11', 1323], ['e10', 1328], ['e09', 1326], ['e05', 1330], ['e29', 1278], ['e26', 1323]],\n    [500, 199]],\n   {'e05': 500, 'e10': 199}),\n  ('second regression',\n   [[['e16', 1328], ['e10', 1278], ['e14', 1333], ['e23', 1278], ['e22', 1333], ['e03', 1326], ['e07', 1333],\n     ['e28', 1323]],\n    [500, 2]],\n   {'e07': 168, 'e14': 167, 'e22': 167}),\n  ('normal control 1', [[['e14', 1420], ['e05', 1420], ['e03', 1415], ['e12', 1415]], [2, 2]],\n   {'e05': 2, 'e14': 2}),\n  ('normal control 2',\n   [[['e12', 1417], ['e06', 1420], ['e29', 1410], ['e28', 1420], ['e18', 1417], ['e11', 1410]], [199, 199]],\n   {'e06': 199, 'e28': 199}),\n  ('normal control 3', [[['e07', 1328], ['e02', 1333], ['e23', 1278], ['e05', 1333]], [199, 199]],\n   {'e02': 199, 'e05': 199}),\n  ('normal control 4',\n   [[['e07', 1495], ['e17', 1445], ['e22', 1493], ['e29', 1500], ['e09', 1490], ['e20', 1490], ['e15', 1500]],\n    [501, 501]],\n   {'e15': 501, 'e29': 501})],\n [('regression: tied position pool',\n   [[['e18', 1333], ['e22', 1326], ['e11', 1326], ['e16', 1333], ['e28', 1323], ['e13', 1278], ['e07', 1323],\n     ['e26', 1330]],\n    [301, 199, 2]],\n   {'e16': 250, 'e18': 250, 'e26': 2}),\n  ('partial repair probe: tied position pool',\n   [[['e02', 1333], ['e19', 1333], ['e08', 1326], ['e09', 1326], ['e25', 1328], ['e17', 1330], ['e16', 1333]],\n    [301, 100, 100, 2]],\n   {'e02': 167, 'e16': 167, 'e17': 2, 'e19': 167}),\n  ('second regression',\n   [[['e26', 1413], ['e12', 1415], ['e08', 1420], ['e20', 1420], ['e11', 1420], ['e28', 1365], ['e23', 1413]],\n    [250, 250, 199]],\n   {'e08': 233, 'e11': 233, 'e20': 233}),\n  ('normal control 1',\n   [[['e14', 1500], ['e27', 1497], ['e13', 1495], ['e03', 1493], ['e02', 1500]], [77, 77]],\n   {'e02': 77, 'e14': 77}),\n  ('normal control 2',\n   [[['e03', 1415], ['e21', 1420], ['e25', 1410], ['e13', 1420], ['e24', 1413], ['e26', 1415], ['e01', 1410]],\n    [301, 301]],\n   {'e13': 301, 'e21': 301}),\n  ('normal control 3',\n   [[['e28', 1420], ['e02', 1417], ['e23', 1420], ['e26', 1417], ['e16', 1417], ['e18', 1417]], [301, 301]],\n   {'e23': 301, 'e28': 301}),\n  ('normal control 4',\n   [[['e02', 1490], ['e09', 1500], ['e28', 1500], ['e29', 1497], ['e12', 1445], ['e19', 1490]], [301, 301]],\n   {'e09': 301, 'e28': 301})]]\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 scoring contract stipulated for this example; it is not the rulebook of any real fantasy platform. 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-fantasy-sports-scoring-contest-payout-ties-tied-position-pool","generated_at":"2026-09-29T14:50:36.620674+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Tie splitting in paid contests moves real money; cent remainders and the pooled positions must be exact.","root_cause":"The tie group is paid the top occupied position times the group size instead of pooling the occupied positions.","sha256":"9dc19aa6d0f7c70c6d65b524eacfeecb30aa9838b5ad838785686977b32998dd","title":"Every tied entry paid the highest shared prize · 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":38.48,"exit_code":1,"observations":[{"actual":{"e06":138,"e21":138},"check":"regression: tied position pool","expected":{"e06":138,"e21":138},"passed":true},{"actual":{"e13":100,"e15":1100},"check":"partial repair probe: tied position pool","expected":{"e13":100,"e15":1000},"passed":false},{"actual":{"e09":151,"e11":151},"check":"second regression","expected":{"e09":151,"e11":151},"passed":true},{"actual":{"e07":77,"e21":77},"check":"normal control 1","expected":{"e07":77,"e21":77},"passed":true},{"actual":{"e20":501,"e23":501},"check":"normal control 2","expected":{"e20":501,"e23":501},"passed":true},{"actual":{"e17":301,"e24":301},"check":"normal control 3","expected":{"e17":301,"e24":301},"passed":true},{"actual":{"e11":500,"e18":500,"e27":500},"check":"normal control 4","expected":{"e11":500,"e18":500,"e27":500},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tied position pool\", \"actual\": {\"e06\": 138, \"e21\": 138}, \"expected\": {\"e06\": 138, \"e21\": 138}, \"passed\": true}, {\"check\": \"partial repair probe: tied position pool\", \"actual\": {\"e15\": 1100, \"e13\": 100}, \"expected\": {\"e13\": 100, \"e15\": 1000}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"e09\": 151, \"e11\": 151}, \"expected\": {\"e09\": 151, \"e11\": 151}, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": {\"e07\": 77, \"e21\": 77}, \"expected\": {\"e07\": 77, \"e21\": 77}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"e20\": 501, \"e23\": 501}, \"expected\": {\"e20\": 501, \"e23\": 501}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"e17\": 301, \"e24\": 301}, \"expected\": {\"e17\": 301, \"e24\": 301}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"e11\": 500, \"e18\": 500, \"e27\": 500}, \"expected\": {\"e11\": 500, \"e18\": 500, \"e27\": 500}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.039,"exit_code":1,"observations":[{"actual":{"e06":199,"e21":199},"check":"regression: tied position pool","expected":{"e06":138,"e21":138},"passed":false},{"actual":{"e13":100,"e15":1000},"check":"partial repair probe: tied position pool","expected":{"e13":100,"e15":1000},"passed":true},{"actual":{"e09":301,"e11":301},"check":"second regression","expected":{"e09":151,"e11":151},"passed":false},{"actual":{"e07":77,"e21":77},"check":"normal control 1","expected":{"e07":77,"e21":77},"passed":true},{"actual":{"e20":501,"e23":501},"check":"normal control 2","expected":{"e20":501,"e23":501},"passed":true},{"actual":{"e17":301,"e24":301},"check":"normal control 3","expected":{"e17":301,"e24":301},"passed":true},{"actual":{"e11":500,"e18":500,"e27":500},"check":"normal control 4","expected":{"e11":500,"e18":500,"e27":500},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tied position pool\", \"actual\": {\"e06\": 199, \"e21\": 199}, \"expected\": {\"e06\": 138, \"e21\": 138}, \"passed\": false}, {\"check\": \"partial repair probe: tied position pool\", \"actual\": {\"e15\": 1000, \"e13\": 100}, \"expected\": {\"e13\": 100, \"e15\": 1000}, \"passed\": true}, {\"check\": \"second regression\", \"actual\": {\"e09\": 301, \"e11\": 301}, \"expected\": {\"e09\": 151, \"e11\": 151}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"e07\": 77, \"e21\": 77}, \"expected\": {\"e07\": 77, \"e21\": 77}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"e20\": 501, \"e23\": 501}, \"expected\": {\"e20\": 501, \"e23\": 501}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"e17\": 301, \"e24\": 301}, \"expected\": {\"e17\": 301, \"e24\": 301}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"e11\": 500, \"e18\": 500, \"e27\": 500}, \"expected\": {\"e11\": 500, \"e18\": 500, \"e27\": 500}, \"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."}}