v1.1: full gene space + specificity z-score; hydroxyurea recovers

Post-hoc improvement after the pre-registered v1 recovery test failed.
Two changes, diagnosing v1's failure:
- score on the full 12,328-gene LINCS space (week2_lincs_extract.py),
  lifting signature overlap from 12% to 85% (brings erythroid markers in)
- src/scoring.py: KS connectivity + per-drug specificity z-score
  (spec_z = SDs below a 1,000 random-query null). Primary ranking is
  now spec_z. (Textbook tau saturated at +/-100 for a coherent query —
  documented; needs a reference-signature library, a v2 item.)
- week3_scoring.py: spec_z primary + WTCS reference + prior-blended
- tests: tau/spec_z calibration test; 19 passing
- scripts/exp_genespace.py: the BING vs all-12,328 comparison

Result: hydroxyurea recovers (rank 40 -> 18, top 6%, passes top-10%),
confirming the v1 failure was the landmark bottleneck not the algorithm.
Overall STILL FAILS: L-glutamine does not reverse (rank 213, metabolite),
and negative controls (norethindrone, ciprofloxacin) rank top-3 —
connectivity != therapeutic relatedness. v1.1 is post-hoc/exploratory,
not a confirmatory test; reported as such in recovery_test_report.md.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/week4_recovery_test.py

@@ -36,6 +36,7 @@ def main() -> None:
         return int(df.loc[name, "rank"]) if name in df.index else None
 
     hu, glut = rk("hydroxyurea"), rk("glutamine")
+    glut_z = df.loc["glutamine", "spec_z"]
 
     # pick negative controls present in the ranking
     negs = {}
@@ -47,9 +48,8 @@ def main() -> None:
     print("=" * 60)
     print(f"N = {n}; top10 cut = {top10_cut}, top25 cut = {top25_cut}, bottom-half > {half}")
     print(f"\nhydroxyurea: rank {hu} (top {100*hu/n:.1f}%)  -> top-10%? {hu <= top10_cut}")
-    glut_score = df.loc["glutamine", "connectivity_score"]
-    print(f"L-glutamine: rank {glut} (top {100*glut/n:.1f}%), WTCS={glut_score:.3f}  "
-          f"-> top-25%? {glut <= top25_cut}  (has signature, so NOT 'missing-signature unscorable')")
+    print(f"L-glutamine: rank {glut} (top {100*glut/n:.1f}%), spec_z={glut_z:+.2f}  "
+          f"-> top-25%? {glut <= top25_cut}  (positive z => does not reverse; has a signature)")
     print("\nnegative controls (pre-specified, 1 per category):")
     n_bottom = 0
     for d, (cat, r) in negs.items():
@@ -70,10 +70,10 @@ def main() -> None:
     print(f"\nsecondary (mechanistic-prior) ranking: hydroxyurea blended_rank {hu_b} "
           f"(top {100*hu_b/n:.1f}%)")
 
-    print("\n--- TOP 10 (raw connectivity) ---")
-    top10 = df.nsmallest(10, "connectivity_score")
+    print("\n--- TOP 10 (primary spec_z ranking) ---")
+    top10 = df.sort_values("rank").head(10)
     for name, r in top10.iterrows():
-        print(f"  {int(r['rank']):2d}  {name:18s} {r['connectivity_score']:+.3f}  "
+        print(f"  {int(r['rank']):2d}  {name:18s} z={r['spec_z']:+.2f}  "
               f"[{r['inclusion_reason']}]  {str(r['known_targets'])[:45]}")