{
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    "schema": "fraud-skew/1",
    "archetype": "claim_vs_effect",
    "issued_at": "2026-09-16T19:18:24.696912+00:00",
    "subject": {
      "model": "xgboost (production-configured, scale_pos_weight)",
      "dataset": "ULB MLG 'Credit Card Fraud Detection' (284,807 anonymized European card transactions, Sept 2013)",
      "rows": 284807,
      "fraud_rate": 0.0017,
      "split": "time-sorted 50/50 (train on earlier transactions, test on later)"
    },
    "claim": "the deployed fraud model achieves its validated performance",
    "verdict": "not_falsified",
    "finding": {
      "gap": "training_serving_skew",
      "validated_f1": 0.7649572649572649,
      "effect": {
        "amount_unit_bug": "f1 0.753 (recall 0.798)",
        "amount_mean_shift": "f1 0.761 (recall 0.798)",
        "amount_scale_bug": "f1 0.773 (recall 0.807)",
        "amount_dropped": "f1 0.781 (recall 0.798)"
      },
      "detail": "perturbing the amount (unit/scale/drop) moves f1 only 0.012 (0.765 \u2192 0.753): the amount is not the dominant signal in a PCA-based fraud model, so the 'f1 collapses' result was an artifact of the synthetic dataset"
    },
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