[cschanhniem]

Phase F F9 — Negative-Result Dashboard from Toy Data

Change

Adds a negative-result dashboard CLI that reads a collection of negative-result entries and produces a structured JSON + Markdown dashboard with summary statistics, score distributions, per-category cross-analysis, and pipeline insights.

What was added

• scripts/negative_result_dashboard.py — CLI that builds and outputs the dashboard
• schemas/negative_result_dashboard.schema.json — JSON Schema for dashboard output
• examples/negative_result_dashboard_example.json — 15 toy entries across all 6 failure categories
• tests/evidence/test_negative_result_dashboard.py — 33 tests
• Makefile — negative-result-dashboard target
• docs/evidence/METRICS_CURRENT.md — changelog updated, test count 2883
• docs/research/ROADMAP.md — Loop 79 entry
• tests/test_test_count_regression.py — baseline updated to 2883

Why it matters

Makes failure patterns reviewable at a glance. Instead of reading individual negative-result entries, reviewers and agents can see category distributions, score trends, and recalibration opportunities in a single structured report. This shows the value of failures by aggregating them into actionable insights.

Safety impact

None. Dashboard is informational only and carries explicit caveats. Does not change any pipeline scoring, selection, or release logic.

Proof level

Level 1 (computational artifact). Dashboard aggregates existing entries; no new biological claims.

Limitations

• Summaries are computational aggregations, not biological measurements
• Category counts reflect pipeline prioritisation, not intrinsic failure rates
• Score distributions are predicted, not measured
• Requires qualified human review for any conclusions

Human review needed

No