Severity and confidence metadata for review findings

A code-review automation surfaces findings to engineers; the team complains that noisy findings drown out the genuine bugs. The team is unwilling to lose any genuine bug, so they want to keep recall high but reduce the noise downstream. Which output design best supports this tradeoff while keeping all of Claude's findings available for later analysis?

1. A.Ask Claude to emit each finding with `severity` (low/med/high) and `confidence` (0–1) fields, and threshold programmatically downstream.
2. B.Lower Claude's temperature and tell it "only report bugs you are highly confident about."
3. C.Have Claude self-filter and emit only findings it would tag as high severity.
4. D.Wrap Claude's review in a second pass that re-reads each finding and keeps only the ones flagged "true positive."