Refresh IPO analysis model calibration

Request:
- Re-analyze the IPO model using the updated historical archive after T1 demand backfill.

Changes:
- Regenerate the v0 analysis dataset from the current SQLite archive.
- Refresh the v0 calibration report with expanded T1 coverage and new empirical bucket rates.
- Update the report template to show pending T1 rows and field-level blanks.
- Clarify v0 limitations and record why the score formula stays unchanged for this refresh.

Verification:
- Ran scripts/build_analysis_dataset.py against data/hk_ipo.sqlite.
- Ran py_compile for scripts/build_analysis_dataset.py.
- Checked dataset row count, T1 demand coverage, source-only T1 gaps, and repo-relative paths.
- Ran git diff --check.

Next useful context:
- T1 structured coverage is now 291 rows, with 06106 and 06675 still pending_not_due.
- The high-conviction T1 bucket remains differentiated, but middle and low buckets are still not monotonic enough for a v1 rule change.

rules/ipo_score_v0.yaml

@@ -143,8 +143,8 @@ calibration_policy:
   dataset_path: data/snapshots/analysis_model_v0_dataset.csv
 
 known_limitations:
-  - T0 has weaker discrimination than T1 because demand data is not available yet.
-  - T1 structured demand facts are incomplete for older HTML-only allotment notices.
+  - T0 has weaker discrimination than T1 because subscription demand is not available at prospectus stage.
+  - T1 is structurally complete for listed rows, but some individual demand fields remain null when the archived source does not explicitly state them.
   - Industry labels and issuer fundamentals are not yet sufficiently structured for v0.
   - T2 grey-market signal is intentionally disabled until a reliable source exists.
   - Current D1 outcomes are from archived market data and should be reviewed for extreme corporate-action or data-vendor anomalies.