* feat(recall): add proof_count boost to combined scoring
Observations with more supporting evidence now rank slightly higher
in recall results. proof_count is threaded through the retrieval
pipeline and applied as a multiplicative boost in reranking:
- types.py: add proof_count field to RetrievalResult
- retrieval.py: include proof_count in SELECT columns
- reranking.py: add log1p-normalized proof_count boost (alpha=0.1)
The boost uses the same multiplicative pattern as recency and temporal
signals. proof_count=1 is neutral, proof_count=50 gives ~+5% boost.
Non-observation fact types are unaffected (neutral 0.5).
* fix(retrieval): Apply proof_count boost to graph and temporal retrieval, normalize scaling
* fix(retrieval): correct proof_norm math to zero-center at count 1
* fix(retrieval): Apply proof_count boost to link_expansion retrieval
* fix: remove BFS zombie, clamp proof_norm to [0,1], fix test comment (log1p->math.log)
