feat: persist and load explained variance for scree plots

- compute_svd_for_window now computes explained variance ratio (s²/sum(s²))
  and appends it as a metadata row (entity_type='metadata',
  entity_id='explained_variance') to motion_rows
- load_scree_data reads this metadata row from svd_vectors instead of
  querying the non-existent sv_metadata column
- run_svd_for_window counts only entity_type='motion' rows in stored_motion
  so metadata rows don't inflate the count
- Added 5 TDD tests covering load, compute, store, and round-trip

All 227 tests pass.

analysis/explorer_data.py

@@ -346,14 +346,28 @@ def load_party_mp_vectors(db_path: str) -> Dict[str, List[np.ndarray]]:
 
 
 def load_scree_data(db_path: str) -> List[float]:
-    """Load scree plot data (explained variance) for current_parliament.
+    """Load scree plot data (explained variance) for current_parliament."""
+    try:
+        con = duckdb.connect(database=db_path, read_only=True)
+        row = con.execute(
+            """
+            SELECT vector FROM svd_vectors
+            WHERE window_id = 'current_parliament'
+              AND entity_type = 'metadata'
+              AND entity_id = 'explained_variance'
+            LIMIT 1
+            """
+        ).fetchone()
+        con.close()
 
-    TODO: Scree data requires SVD metadata (singular values / explained
-    variance ratios) to be stored in the database. Currently only
-    transformed vectors are stored in svd_vectors.vector, not the
-    decomposition metadata needed for a scree plot.
-    """
-    return []
+        if row and row[0]:
+            import json
+
+            return json.loads(row[0])
+        return []
+    except Exception:
+        logger.exception("Failed to load scree data")
+        return []
 
 
 def load_motions_df(db_path: str) -> pd.DataFrame: