# Probe 6 -- AI-Contamination Miss

**Probe ID:** BSP-06-AI-CONTAMINATION-MISS
**Probe version:** 1.0
**Position in fixed probe order:** 6 of 6

---

## Definition

An AI-contamination miss occurs when RAM treats AI-generated or AI-laundered material as primary evidence without disclosing the AI-generation origin. The defect includes: citation of known AI-content domains; citation of an AI-generated intermediate (e.g., an LLM-written "systematic review") where primary sources were expected; and source-to-source pattern-matching signatures suggestive of AI-laundering through apparent-authority venues.

This probe has the shortest evidence maturity of the six; AI-content detection heuristics shift rapidly. The probe version is tracked explicitly per run so Phase 4 can schedule heuristic refreshes without confusing historical detections.

## Trigger conditions

Fire on any of:

- Known AI-domain citation -- a citation's domain matches the current AI-content registry (maintained via Frame Host retrieval hooks). Registry is versioned; record registry version used.
- AI-generated intermediate in citation chain -- the citation is itself a generated synthesis (LLM-written overview, AI-generated encyclopedia entry, auto-translated summary) standing in for a primary source.
- Source-to-source pattern match -- multiple cited sources share stylistic, structural, or phrasing signatures inconsistent with independent authorship (classic laundering signal). Use embedding-similarity threshold calibrated per Phase 9 harness.
- Missing provenance -- citation lacks author, institutional affiliation, publication venue, or date sufficient to rule out AI origin; critical for citations ranked in the top-3 evidence weights.

Severity grading: known AI-domain, non-load-bearing citation -> medium; AI-generated intermediate, non-load-bearing -> medium; known AI-domain, load-bearing -> high; AI-generated intermediate, load-bearing -> critical; source-to-source laundering pattern confirmed -> critical.

## Method

1. Retrieve current AI-content registry via Frame Host (Evaluator retrieval, not RAM). Record registry version.
2. For each citation, match domain against registry; flag hits.
3. For each citation, classify as primary / intermediate / generated-synthesis using the registry's intermediate taxonomy.
4. Run embedding-similarity scan across RAM's source set; flag clusters exceeding the Phase 9-calibrated threshold (default: cosine similarity > 0.85 on normalized content).
5. For each load-bearing citation (top-3 by weight), require full provenance (author, affiliation, venue, date); missing fields fire.
6. If the AI-content registry is unavailable, emit null-run record and escalate to E9. Do NOT approximate via heuristics if the registry is down; the registry is the calibration anchor.

## Evidence requirement

- `registry_version` -- AI-content registry version used.
- `citations_checked` -- integer.
- `domain_hits` -- per-hit record.
- `intermediate_hits` -- per-hit record with intermediate-type classification.
- `laundering_clusters` -- embedding-similarity clusters with member citations.
- `provenance_gaps` -- per-citation missing-field list for load-bearing citations.
- `embedding_threshold_used` -- float; source (Phase 9 calibration record id).

## Output record schema

```
{
  "probe_id": "BSP-06-AI-CONTAMINATION-MISS",
  "probe_version": "1.0",
  "registry_version": <string>,
  "fired": true|false,
  "severity": "low"|"medium"|"high"|"critical",
  "claim_id": <string>,
  "run_id": <string>,
  "hit_count": <int>,
  "laundering_cluster_count": <int>,
  "evidence": { ... as above ... },
  "taxonomy_entry_candidate": <taxonomy entry id or "new">
}
```

## Anti-examples

- Do NOT use RAM's own AI-detection output; this is the defining zero-overlap case.
- Do NOT fire on "AI-assisted" human work when the human work is the load-bearing authorship; restrict to AI-generated or AI-laundered.
- Do NOT skip the probe when registry is stale; emit null-run and flag registry staleness to E9.
- Do NOT roll laundering clusters into monoculture; laundering is an authenticity defect, monoculture is a concentration defect. They can co-fire; record separately.
