diff --git a/LeQua2024/baselines.py b/LeQua2024/baselines.py
index 73f9246..d35e09a 100644
--- a/LeQua2024/baselines.py
+++ b/LeQua2024/baselines.py
@@ -12,7 +12,7 @@ from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation a
 import quapy.functional as F
 
 
-# LeQua official baselines
+# LeQua official baselines (under development!)
 # =================================================================================
 
 BINARY_TASKS = ['T1', 'T4']
@@ -40,12 +40,6 @@ def baselines():
     yield ACC(new_cls()), "ACC", q_params
     yield PCC(new_cls()), "PCC", q_params
     yield PACC(new_cls()), "PACC", q_params
-    # yield EMQ(CalibratedClassifierCV(new_cls())), "SLD-Platt", wrap_params(wrap_params(lr_params, 'estimator'), 'classifier')
-    # yield EMQ(new_cls()), "SLD", q_params
-    # yield EMQ(new_cls()), "SLD-BCTS", {**q_params, 'recalib': ['bcts'], 'val_split': [5]}
-    # yield MLPE(), "MLPE", None
-    # if args.task in BINARY_TASKS:
-    #     yield MS2(new_cls()), "MedianSweep2", q_params
 
 
 def main(args):
@@ -60,6 +54,9 @@ def main(args):
 
     train, gen_val, gen_test = fetch_lequa2024(task=args.task, data_home=args.datadir, merge_T3=True)
 
+    # gen_test is None, since the true prevalence vectors for the test samples will be released
+    # only after the competition ends
+
     print(f'number of classes: {len(train.classes_)}')
     print(f'number of training documents: {len(train)}')
     print(f'training prevalence: {F.strprev(train.prevalence())}')