Consolidate academic results into a single results_academic/ dir

- Replace the old results_academic/ contents with the newer per-model
JSONs previously living under results_academic_new/ (richer schema:
per-fold metrics, ensemble columns for GBDTs, more datasets).
- Update _load_academic_results default directory accordingly.
- Drop complete_results_acad.json (unused since the app moved to the
per-model file loader; no code path still reads it).

The old TabPFN v2.5 / TabICL v1 pretty-name mappings stay: the current
tabpfn.json and tabicl.json files provide those results (the pretty
name is applied via _MODEL_DISPLAY_PRETTY on the filename-derived id).

app.py

@@ -231,14 +231,14 @@ def compute_elo_for_subset(df: pd.DataFrame, metric: str = "Accuracy") -> pd.Dat
     return df
 
 
-# --- Load new academic results from results_academic/*.json ---
+# --- Load academic results from results_academic/*.json ---
 # Each file: {model, summary, datasets: {dataset_id: {folds: [...], mean_auc, mean_accuracy}}}
 # We build BOTH a per-fold long dataframe (for proper CI computation) and a
 # per-dataset mean dataframe (matching the legacy schema used downstream).
 import glob, os
 
 
-def _load_academic_results(directory="results_academic_new"):
+def _load_academic_results(directory="results_academic"):
     """Load per-model JSONs into long dataframes.
 
     Supports both schemas: