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1{2 "critical_violations": [],3 "evaluated_utc": "2026-10-30T04:01:38.447226+00:00",4 "evaluator_version": "F3-dev-01/1.0",5 "notes": "Deterministic evaluation; reproducibility component is filled by the harness. Full-sample re-estimation leaks are NOT detectable on this series (documented in REFERENCE.md); the bound catches smoothing/foresight leaks.",6 "oracle_sha256": "3c7498c1f70346124c8233d46ab30a2eef957ec2e3437b34674287f93b43016a",7 "quality_components": {8  "baseline": 1.0,9  "correctness": 1.0,10  "integrity": 1.0,11  "reporting": 1.0,12  "reproducibility": 1.013 },14 "quality_score": 1.0,15 "receipt_sha256": "316c1851986d82a485e328de56757793af04cdcf1cbf580282b8e10864edcab1",16 "required_criteria": [17  {18   "description": "Naive RMSE and MAE at h=1 equal the exact reference (rel 1e-6); 35 evaluated pairs (target 2025-10 missing)",19   "expected": {20    "mae_naive_h1": 0.09142857142857148,21    "rmse_naive_h1": 0.1171080087538240322   },23   "id": "naive_h1",24   "observed": {25    "mae_naive_h1": 0.09142857142857148,26    "rmse_naive_h1": 0.1171080087538240327   },28   "passed": true,29   "tolerance": {30    "mae_naive_h1": {31     "rel": 1e-0632    },33    "rmse_naive_h1": {34     "rel": 1e-0635    }36   }37  },38  {39   "description": "Naive RMSE and MAE at h=3 equal the exact reference (rel 1e-6)",40   "expected": {41    "mae_naive_h3": 0.12000000000000005,42    "rmse_naive_h3": 0.1585649934344184343   },44   "id": "naive_h3",45   "observed": {46    "mae_naive_h3": 0.12000000000000005,47    "rmse_naive_h3": 0.1585649934344184348   },49   "passed": true,50   "tolerance": {51    "mae_naive_h3": {52     "rel": 1e-0653    },54    "rmse_naive_h3": {55     "rel": 1e-0656    }57   }58  },59  {60   "description": "n_origins = 36, n_evaluated_h1 = 35, n_evaluated_h3 = 35",61   "expected": {62    "n_evaluated_h1": 35,63    "n_evaluated_h3": 35,64    "n_origins": 3665   },66   "id": "counts",67   "observed": {68    "n_evaluated_h1": 35,69    "n_evaluated_h3": 35,70    "n_origins": 3671   },72   "passed": true,73   "tolerance": {74    "n_evaluated_h1": {75     "abs": 076    },77    "n_evaluated_h3": {78     "abs": 079    },80    "n_origins": {81     "abs": 082    }83   }84  },85  {86   "description": "forecasts.csv: 144 rows, target = origin + h, naive forecast = last observation <= origin, actual matches data",87   "expected": {88    "n_rows": 14489   },90   "id": "forecasts_chronology",91   "observed": {92    "actual_mismatches": 0,93    "model_rows": 72,94    "n_rows": 144,95    "naive_rows": 7296   },97   "passed": true,98   "tolerance": null99  },100  {101   "description": "model RMSE/MAE in results.json agree with forecasts.csv (rel 1e-5)",102   "expected": "recomputed from forecasts.csv",103   "id": "model_errors_consistent",104   "observed": {105    "h1": {106     "mae_from_csv": 0.09561928226000001,107     "mae_reported": 0.09561928226000001,108     "n": 35,109     "rmse_from_csv": 0.1197904141277979,110     "rmse_reported": 0.1197904141277979111    },112    "h3": {113     "mae_from_csv": 0.12090394601714284,114     "mae_reported": 0.12090394601714284,115     "n": 35,116     "rmse_from_csv": 0.15937419012351917,117     "rmse_reported": 0.15937419012351917118    }119   },120   "passed": true,121   "tolerance": {122    "rel": 1e-05123   }124  },125  {126   "description": "rel_improvement_rmse_h* = 1 - rmse_model/rmse_naive (abs 1e-6)",127   "expected": null,128   "id": "rel_improvement_consistent",129   "observed": {130    "h1": {131     "expected": -0.022905396501212927,132     "reported": -0.022905396501212927133    },134    "h3": {135     "expected": -0.005103249283300526,136     "reported": -0.005103249283300526137    }138   },139   "passed": true,140   "tolerance": {141    "abs": 1e-06142   }143  },144  {145   "description": "data_issues mentions the missing 2025-10 observation",146   "expected": "mention of 2025-10",147   "id": "data_issue_reported",148   "observed": "[{\"issue\": \"Missing observation(s) in UNRATE.csv: ['2025-10-01']\", \"handling\": \"Naive at origin 2025-10-01 uses the 2025-09-01 value; the ARIMA likelihood/filter skips the missing month; (origin,horizon) pairs targeting it are kept in forecasts.csv with empty actual and excluded from error statistic",149   "passed": true,150   "tolerance": null151  }152 ],153 "run_id": "RP0021",154 "schema_error": "",155 "schema_valid": true,156 "scientific_success": true,157 "task_id": "F3-dev-01"158}
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