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synthetic_audit_result.json
{
"purpose": "Reproduction of 15 October 2026 synthetic stress audit, not production accuracy",
"python": "3.12.14",
"sklearn": "1.8.0",
"engine_sha256": "944dee92eec24929fa1f1a87ba3b1e8af71b10ea3da1d3ab2c1f136f7747382f",
"fixture_sha256": "36b7978b487775dc9926c5ecaf7aba3eddf494a4ee8136c855b19b48af1c24b4",
"groups": [
{
"name": "ordinary_by_construction",
"rows": 25,
"model_flags": 0
},
{
"name": "extreme_unit_amount",
"rows": 10,
"model_flags": 10
},
{
"name": "plausible_numbers_with_unverifiable_fact",
"rows": 5,
"model_flags": 0
}
],
"preservation": {
"input_unchanged": true,
"rows": 40,
"original_objects_order_and_text_ids_preserved": true
},
"limitations": [
"All labels are synthetic construction choices, not independent reviewer judgments.",
"Counts are not population false-positive rates, recall, or business accuracy.",
"A numeric outlier model has no evidence to verify the business fact in a note.",
"The 0.6 conservative policy requires both a model outlier and a feature outside reference support; this audit is a development fixture, not independent validation.",
"Model flags are review hints, not findings of an error or fraud."
],
"report": {
"tool": "ReviewQueue 0.6",
"model": "scikit-learn IsolationForest",
"features": [
"signed_log_amount",
"log_quantity",
"signed_log_unit_amount"
],
"reference_models": {
"USD": {
"reference_rows": 55,
"status": "trained",
"flag_policy": "model_outlier_and_outside_reference_support",
"feature_bounds": [
[
4.51085950651685,
6.311734809152915
],
[
0.6931471805599453,
1.791759469228055
],
[
4.51085950651685,
4.709530201312334
]
],
"policy_notice": "Conservative review policy; can miss interior anomalies. Not a calibrated probability."
}
},
"input_rows": 40,
"output_rows": 40,
"review_rows": 10,
"notice": "Review hints only. Scores are not probabilities. No fraud, accounting, legal, or payment decision is made.",
"rows": [
{
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"original": {
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"currency": "USD",
"amount": "91",
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},
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},
{
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