Tune the semantic-fusion weight to 2.0

Equal-weight reciprocal-rank fusion diluted strong semantic hits when
BM25 ranked the same provision poorly. W_SEM=2.0 lifts the 89-question
eval -- Hit@1 0.57->0.65, Hit@5 0.88->0.90, MRR 0.70->0.75 -- with no
regression. See precision-findings.md.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

canlex/index.py

@@ -13,7 +13,7 @@ from .synonyms import expand_query
 K1 = 1.5
 B = 0.75
 RRF_K = 60          # reciprocal-rank-fusion damping constant
-W_SEM = 1.0         # weight on the semantic retriever in the fusion (1.0 = equal)
+W_SEM = 2.0         # weight on the semantic retriever in the fusion (1.0 = equal; eval-tuned)
 CANDIDATES = 80     # hits each retriever contributes to the fusion
 RERANK_POOL = 50    # top fused candidates the cross-encoder rescores
 SOURCE_CAP = 2      # max chunks one case/memo/agreement/directive may contribute