{"abstract":"Constant vector loses source row ordinals in output.","category":"Data systems","checks":7,"contract":"Materialize a constant vector over a requested logical length, combining scalar validity with row validity. A selection chooses logical rows and may repeat them. Return [row-index,value-or-null] for every selected row; scalar zero is a valid value.","contract_signature":"d","evaluation_group":"s3-data-systems-constant-vector-materialize","failed_approach":"Using a constant ordinal still disconnects values from selected rows.","family":"s3-data-systems-constant-vector-materialize-row-label","id":"FA-45086","implementations":{"attempt":{"sha256":"415b29bd7b5fe6441213b044e3b208f976691b6872b4ba1e8a7df98f114b6f25","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(d):\n    try:\n        value,scalar_valid,row_valid,selection=d\n        out=[]\n        for row in selection:\n            known=scalar_valid and row_valid[row]\n            out.append([0,value if known else None])\n        return out\n    except (IndexError, KeyError, ValueError, StopIteration) as exc:\n        return {\"representation_error\": type(exc).__name__}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nif N == 1:\n    check('selected validity', solve([1, True, [True, False, True], [2, 1, 0]]), [[2, 1], [1, None], [0, 1]])\n    check('scalar null', solve([1, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([1, True, [False, True], [1, 0]]), [[1, 1], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([1, True, [True, True], [1, 1]]), [[1, 1], [1, 1]])\n    check('empty selection', solve([1, True, [True], []]), [])\n    check('single row control', solve([1, True, [True], [0]]), [[0, 1]])\nelif N == 2:\n    check('selected validity', solve([2, True, [True, False, True], [2, 1, 0]]), [[2, 2], [1, None], [0, 2]])\n    check('scalar null', solve([2, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([2, True, [False, True], [1, 0]]), [[1, 2], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([2, True, [True, True], [1, 1]]), [[1, 2], [1, 2]])\n    check('empty selection', solve([2, True, [True], []]), [])\n    check('single row control', solve([2, True, [True], [0]]), [[0, 2]])\nelif N == 3:\n    check('selected validity', solve([3, True, [True, False, True], [2, 1, 0]]), [[2, 3], [1, None], [0, 3]])\n    check('scalar null', solve([3, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([3, True, [False, True], [1, 0]]), [[1, 3], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([3, True, [True, True], [1, 1]]), [[1, 3], [1, 3]])\n    check('empty selection', solve([3, True, [True], []]), [])\n    check('single row control', solve([3, True, [True], [0]]), [[0, 3]])\nelif N == 4:\n    check('selected validity', solve([4, True, [True, False, True], [2, 1, 0]]), [[2, 4], [1, None], [0, 4]])\n    check('scalar null', solve([4, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([4, True, [False, True], [1, 0]]), [[1, 4], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([4, True, [True, True], [1, 1]]), [[1, 4], [1, 4]])\n    check('empty selection', solve([4, True, [True], []]), [])\n    check('single row control', solve([4, True, [True], [0]]), [[0, 4]])\nelif N == 5:\n    check('selected validity', solve([5, True, [True, False, True], [2, 1, 0]]), [[2, 5], [1, None], [0, 5]])\n    check('scalar null', solve([5, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([5, True, [False, True], [1, 0]]), [[1, 5], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([5, True, [True, True], [1, 1]]), [[1, 5], [1, 5]])\n    check('empty selection', solve([5, True, [True], []]), [])\n    check('single row control', solve([5, True, [True], [0]]), [[0, 5]])\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":"97672960d9a5099c3e4cfcca2247ab7515754dad404e2a7c082ca2885f9e6150","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(d):\n    try:\n        value,scalar_valid,row_valid,selection=d\n        out=[]\n        for row in selection:\n            known=scalar_valid and row_valid[row]\n            out.append([len(out),value if known else None])\n        return out\n    except (IndexError, KeyError, ValueError, StopIteration) as exc:\n        return {\"representation_error\": type(exc).__name__}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nif N == 1:\n    check('selected validity', solve([1, True, [True, False, True], [2, 1, 0]]), [[2, 1], [1, None], [0, 1]])\n    check('scalar null', solve([1, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([1, True, [False, True], [1, 0]]), [[1, 1], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([1, True, [True, True], [1, 1]]), [[1, 1], [1, 1]])\n    check('empty selection', solve([1, True, [True], []]), [])\n    check('single row control', solve([1, True, [True], [0]]), [[0, 1]])\nelif N == 2:\n    check('selected validity', solve([2, True, [True, False, True], [2, 1, 0]]), [[2, 2], [1, None], [0, 2]])\n    check('scalar null', solve([2, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([2, True, [False, True], [1, 0]]), [[1, 2], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([2, True, [True, True], [1, 1]]), [[1, 2], [1, 2]])\n    check('empty selection', solve([2, True, [True], []]), [])\n    check('single row control', solve([2, True, [True], [0]]), [[0, 2]])\nelif N == 3:\n    check('selected validity', solve([3, True, [True, False, True], [2, 1, 0]]), [[2, 3], [1, None], [0, 3]])\n    check('scalar null', solve([3, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([3, True, [False, True], [1, 0]]), [[1, 3], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([3, True, [True, True], [1, 1]]), [[1, 3], [1, 3]])\n    check('empty selection', solve([3, True, [True], []]), [])\n    check('single row control', solve([3, True, [True], [0]]), [[0, 3]])\nelif N == 4:\n    check('selected validity', solve([4, True, [True, False, True], [2, 1, 0]]), [[2, 4], [1, None], [0, 4]])\n    check('scalar null', solve([4, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([4, True, [False, True], [1, 0]]), [[1, 4], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([4, True, [True, True], [1, 1]]), [[1, 4], [1, 4]])\n    check('empty selection', solve([4, True, [True], []]), [])\n    check('single row control', solve([4, True, [True], [0]]), [[0, 4]])\nelif N == 5:\n    check('selected validity', solve([5, True, [True, False, True], [2, 1, 0]]), [[2, 5], [1, None], [0, 5]])\n    check('scalar null', solve([5, False, [True, True], [0, 1]]), [[0, None], [1, None]])\n    check('mixed row null', solve([5, True, [False, True], [1, 0]]), [[1, 5], [0, None]])\n    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])\n    check('repeat row', solve([5, True, [True, True], [1, 1]]), [[1, 5], [1, 5]])\n    check('empty selection', solve([5, True, [True], []]), [])\n    check('single row control', solve([5, True, [True], [0]]), [[0, 5]])\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":"Offline stipulated semantics over valid small inputs; no performance, concurrency, or production-engine conformance claim. 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":"s3-data-systems-constant-vector-materialize-row-label","generated_at":"2026-09-29T14:44:18.642829+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A bounded deterministic data engine model makes representation and changelog faults reproducible.","root_cause":"constant-vector-materialize: Constant vector loses source row ordinals in output.","sha256":"f8cfcaee3e2460b63647fa77397f98b6474ccb6bc06583483f00f0aeac37267c","title":"Constant vector loses source row ordinals in output · 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":45.567,"exit_code":1,"observations":[{"actual":[[0,1],[0,null],[0,1]],"check":"selected validity","expected":[[2,1],[1,null],[0,1]],"passed":false},{"actual":[[0,null],[0,null]],"check":"scalar null","expected":[[0,null],[1,null]],"passed":false},{"actual":[[0,1],[0,null]],"check":"mixed row null","expected":[[1,1],[0,null]],"passed":false},{"actual":[[0,0]],"check":"zero constant","expected":[[0,0]],"passed":true},{"actual":[[0,1],[0,1]],"check":"repeat row","expected":[[1,1],[1,1]],"passed":false},{"actual":[],"check":"empty selection","expected":[],"passed":true},{"actual":[[0,1]],"check":"single row control","expected":[[0,1]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"selected validity\", \"actual\": [[0, 1], [0, null], [0, 1]], \"expected\": [[2, 1], [1, null], [0, 1]], \"passed\": false}, {\"check\": \"scalar null\", \"actual\": [[0, null], [0, null]], \"expected\": [[0, null], [1, null]], \"passed\": false}, {\"check\": \"mixed row null\", \"actual\": [[0, 1], [0, null]], \"expected\": [[1, 1], [0, null]], \"passed\": false}, {\"check\": \"zero constant\", \"actual\": [[0, 0]], \"expected\": [[0, 0]], \"passed\": true}, {\"check\": \"repeat row\", \"actual\": [[0, 1], [0, 1]], \"expected\": [[1, 1], [1, 1]], \"passed\": false}, {\"check\": \"empty selection\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single row control\", \"actual\": [[0, 1]], \"expected\": [[0, 1]], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":44.31,"exit_code":1,"observations":[{"actual":[[0,1],[1,null],[2,1]],"check":"selected validity","expected":[[2,1],[1,null],[0,1]],"passed":false},{"actual":[[0,null],[1,null]],"check":"scalar null","expected":[[0,null],[1,null]],"passed":true},{"actual":[[0,1],[1,null]],"check":"mixed row null","expected":[[1,1],[0,null]],"passed":false},{"actual":[[0,0]],"check":"zero constant","expected":[[0,0]],"passed":true},{"actual":[[0,1],[1,1]],"check":"repeat row","expected":[[1,1],[1,1]],"passed":false},{"actual":[],"check":"empty selection","expected":[],"passed":true},{"actual":[[0,1]],"check":"single row control","expected":[[0,1]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"selected validity\", \"actual\": [[0, 1], [1, null], [2, 1]], \"expected\": [[2, 1], [1, null], [0, 1]], \"passed\": false}, {\"check\": \"scalar null\", \"actual\": [[0, null], [1, null]], \"expected\": [[0, null], [1, null]], \"passed\": true}, {\"check\": \"mixed row null\", \"actual\": [[0, 1], [1, null]], \"expected\": [[1, 1], [0, null]], \"passed\": false}, {\"check\": \"zero constant\", \"actual\": [[0, 0]], \"expected\": [[0, 0]], \"passed\": true}, {\"check\": \"repeat row\", \"actual\": [[0, 1], [1, 1]], \"expected\": [[1, 1], [1, 1]], \"passed\": false}, {\"check\": \"empty selection\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single row control\", \"actual\": [[0, 1]], \"expected\": [[0, 1]], \"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."}}