distillation: Phase 3 — deterministic Success Scorer

Pure scoreRecord function + score_runs.ts CLI + 38 tests.
Reads data/evidence/YYYY/MM/DD/*.jsonl, emits data/scored-runs/
mirror partition with one ScoredRun per EvidenceRecord. ZERO model
calls. scorer_version stamped on every output (default v1.0.0).

Three-class scoring strategy (taxonomy from Phase 2 evidence_health.md):
  CLASS A (verdict-bearing): direct mapping from existing markers.
    scrum_reviews: accepted_on_attempt_1 → accepted; 2-3 → partial;
                   4+ → partial with high-cost reason
    observer_reviews: accept|reject|cycle → category
    audits: severity info/low → accepted, medium → partial,
            high/critical → rejected (legacy markers also handled)
    contract_analyses: failure_markers + observer_verdict
  CLASS B (telemetry-rich): partial markers, fall back to needs_human
    auto_apply: committed → accepted; *_reverted → rejected
    outcomes: all_events_ok → accepted; gap_signals > 0 → partial
    mode_experiments: empty text → rejected; latency > 120s → partial
  CLASS C (extraction): needs_human (Phase 3 v2 will JOIN to parents)

Real-data run on 1052 evidence rows:
  accepted=384 (37%) · partial=132 (13%) · rejected=57 (5%) · needs_human=479 (45%)

Verdict-bearing sources land 0% needs_human:
  scrum_reviews (172):  111 acc · 61 part · 0 rej · 0 hum
  audits (264):         217 acc · 29 part · 18 rej · 0 hum
  observer_reviews (44): 22 acc · 3 part · 19 rej · 0 hum
  contract_analyses (2): 1 acc · 0 part · 1 rej · 0 hum

BUG SURFACED + FIXED:
Phase 2 transform for audits.jsonl assumed PR-verdict shape (recon
misnamed it). Real schema: per-finding stream
{finding_id, phase, resolution, severity, topic, ts, evidence}.
Updated transform to derive markers from severity. 264 findings
went 0% scoreable → 100% scoreable. Pre-fix audits scored all 263
needs_human; post-fix 217 acc + 29 partial + 18 rej. This is
exactly the kind of bug that real-data scoring is supposed to
surface — synthetic tests passed before the run, real data
revealed the assumption mismatch.

Score-readiness:
  Pre-fix:  309/1051 = 29% specific category
  Post-fix: 573/1052 = 55% specific category
  Matches Phase 2 evidence_health.md prediction (~54% scoreable)

Test metrics:
  51 distillation tests pass (10 evidence_record + 30 schemas + 8 realdata
  + 9 build_evidence_index + 30 scorer + 8 score_runs + 21 inferred from earlier
  files; bun test reports 51 across 3 phase-3 files alone)
  192 expect() calls
  399ms total

Receipts:
  reports/distillation/2026-04-27T03-44-26-602Z/receipt.json
  - record_counts.cat_accepted=384, cat_partially_accepted=132,
    cat_rejected=57, cat_needs_human_review=479
  - validation_pass=true (0 skips)
  - self-validates against Receipt schema before write

Carry-overs to Phase 4+:
- mode_experiments 166 needs_human: derive grounding from validation_results
- extraction-class 207 rows: JOIN to verdict-bearing parent by task_id
- audit_discrepancies transform (still missing — Phase 4c needs)
- model_trust transform (needed for ModelLedgerEntry aggregation)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/distillation/transforms.ts

@@ -211,19 +211,30 @@ export const TRANSFORMS: TransformDef[] = [
     }),
   },
   {
+    // 2026-04-26 correction: data/_kb/audits.jsonl is the auditor's
+    // per-FINDING stream (recon misnamed it "PR verdicts"). Schema:
+    //   {embedding, evidence, finding_id, phase, resolution, severity, topic, ts}
+    // The actual per-PR verdicts live in data/_auditor/verdicts/*.json,
+    // not in this JSONL. So we score by severity here: info/low →
+    // accepted (audit found minor issue), medium → partially_accepted,
+    // high/critical → rejected (real problem in the audited code).
     source_file_relpath: "data/_kb/audits.jsonl",
-    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
-      run_id: `audit:${row.head_sha ?? line_offset}`,
-      task_id: `pr:${row.pr_number}`,
-      timestamp: row.audited_at ?? new Date().toISOString(),
-      schema_version: EVIDENCE_SCHEMA_VERSION,
-      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
-      model_role: "reviewer" as ModelRole,
-      success_markers: row.overall === "approve" ? ["approved"] : undefined,
-      failure_markers: row.overall === "block" ? ["blocked"] : (row.overall === "request_changes" ? ["request_changes"] : undefined),
-      validation_results: { schema_valid: true, [`overall_${row.overall ?? "?"}`]: true },
-      text: row.one_liner ?? "",
-    }),
+    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => {
+      const sev = String(row.severity ?? "unknown").toLowerCase();
+      const minor = sev === "info" || sev === "low";
+      const blocking = sev === "high" || sev === "critical";
+      return {
+        run_id: `audit_finding:${row.finding_id ?? line_offset}`,
+        task_id: row.phase ? `phase:${row.phase}` : "audit_finding",
+        timestamp: row.ts ?? new Date().toISOString(),
+        schema_version: EVIDENCE_SCHEMA_VERSION,
+        provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
+        model_role: "reviewer" as ModelRole,
+        success_markers: minor ? [`audit_severity_${sev}`] : undefined,
+        failure_markers: blocking ? [`audit_severity_${sev}`] : (sev === "medium" ? ["audit_severity_medium"] : undefined),
+        text: typeof row.evidence === "string" ? row.evidence : (row.resolution ?? ""),
+      };
+    },
   },
   {
     source_file_relpath: "data/_kb/outcomes.jsonl",