diff --git a/ClassifierAccuracy/experiments.py b/ClassifierAccuracy/experiments.py
index 3234e99..b4e976d 100644
--- a/ClassifierAccuracy/experiments.py
+++ b/ClassifierAccuracy/experiments.py
@@ -2,7 +2,7 @@ from ClassifierAccuracy.util.commons import *
 from ClassifierAccuracy.util.plotting import plot_diagonal
 
 PROBLEM = 'multiclass'
-ORACLE = False
+ORACLE = True
 basedir = PROBLEM+('-oracle' if ORACLE else '')
 
 
diff --git a/ClassifierAccuracy/util/commons.py b/ClassifierAccuracy/util/commons.py
index 2896b61..55f8aa6 100644
--- a/ClassifierAccuracy/util/commons.py
+++ b/ClassifierAccuracy/util/commons.py
@@ -17,7 +17,7 @@ from ClassifierAccuracy.util.tabular import Table
 from quapy.method.aggregative import EMQ, ACC, KDEyML
 
 from quapy.data import LabelledCollection
-from quapy.data.datasets import fetch_UCIMulticlassLabelledCollection, UCI_MULTICLASS_DATASETS
+from quapy.data.datasets import fetch_UCIMulticlassLabelledCollection, UCI_MULTICLASS_DATASETS, fetch_lequa2022
 from quapy.data.datasets import fetch_reviews
 
 
@@ -43,6 +43,8 @@ def gen_multi_datasets(only_names=False)-> [str,[LabelledCollection,LabelledColl
         else:
             dataset = fetch_UCIMulticlassLabelledCollection(dataset_name)
             yield dataset_name, split(dataset)
+
+    # yields the 20 newsgroups dataset
     train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
     test  = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))
     tfidf = TfidfVectorizer(min_df=5, sublinear_tf=True)
@@ -53,6 +55,10 @@ def gen_multi_datasets(only_names=False)-> [str,[LabelledCollection,LabelledColl
     T, V = train.split_stratified(train_prop=0.5, random_state=0)
     yield "20news", (T, V, U)
 
+    # yields the T1B@LeQua2022 (training) dataset
+    train, _, _ = fetch_lequa2022(task='T1B')
+    yield "T1B-LeQua2022", split(train)
+
 
 
 def gen_bin_datasets(only_names=False) -> [str,[LabelledCollection,LabelledCollection,LabelledCollection]]:
@@ -92,7 +98,7 @@ def gen_CAP(h, acc_fn, with_oracle=False)->[str, ClassifierAccuracyPrediction]:
 def gen_CAP_cont_table(h)->[str,CAPContingencyTable]:
     acc_fn = None
     yield 'Naive', NaiveCAP(h, acc_fn)
-    #yield 'CT-PPS-EMQ', ContTableTransferCAP(h, acc_fn, EMQ(LogisticRegression()))
+    yield 'CT-PPS-EMQ', ContTableTransferCAP(h, acc_fn, EMQ(LogisticRegression()))
     #yield 'CT-PPS-KDE', ContTableTransferCAP(h, acc_fn, KDEyML(LogisticRegression(class_weight='balanced'), bandwidth=0.01))
     yield 'CT-PPS-KDE05', ContTableTransferCAP(h, acc_fn, KDEyML(LogisticRegression(class_weight='balanced'), bandwidth=0.05))
     #yield 'QuAcc(EMQ)nxn-noX', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_posteriors=True, add_X=False)
@@ -308,6 +314,8 @@ def gen_tables(basedir, datasets):
 
     os.makedirs('./tables', exist_ok=True)
 
+    with_oracle = 'oracle' in basedir
+
     tex_doc = """
     \\documentclass[10pt,a4paper]{article}
     \\usepackage[utf8]{inputenc}
@@ -322,40 +330,45 @@ def gen_tables(basedir, datasets):
     \\begin{document}
     """
 
-    classifier = classifiers[0]
-    for metric in [measure for measure, _ in gen_acc_measure()]:
+    for classifier in classifiers:
+        for metric in [measure for measure, _ in gen_acc_measure()]:
 
-        table = Table(datasets, methods, prec_mean=5, clean_zero=True)
-        for method, dataset in itertools.product(methods, datasets):
-            path = getpath(basedir, classifier, metric, dataset, method)
-            if not os.path.exists(path):
-                print('missing ', path)
-                continue
-            results = json.load(open(path, 'r'))
-            true_acc = results['true_acc']
-            estim_acc = np.asarray(results['estim_acc'])
-            if any(np.isnan(estim_acc)):
-                print(f'nan values found in {method=} {dataset=}')
-                continue
-            if any(estim_acc>1.00001):
-                print(f'values >1 found in {method=} {dataset=} [max={estim_acc.max()}]')
-                continue
-            if any(estim_acc<-0.00001):
-                print(f'values <0 found in {method=} {dataset=} [min={estim_acc.min()}]')
-                continue
-            errors = cap_errors(true_acc, estim_acc)
-            table.add(dataset, method, errors)
+            table = Table(datasets, methods, prec_mean=5, clean_zero=True)
+            for method, dataset in itertools.product(methods, datasets):
+                path = getpath(basedir, classifier, metric, dataset, method)
+                if not os.path.exists(path):
+                    print('missing ', path)
+                    continue
+                results = json.load(open(path, 'r'))
+                true_acc = results['true_acc']
+                estim_acc = np.asarray(results['estim_acc'])
+                if any(np.isnan(estim_acc)):
+                    print(f'nan values found in {method=} {dataset=}')
+                    continue
+                if any(estim_acc>1.00001):
+                    print(f'values >1 found in {method=} {dataset=} [max={estim_acc.max()}]')
+                    continue
+                if any(estim_acc<-0.00001):
+                    print(f'values <0 found in {method=} {dataset=} [min={estim_acc.min()}]')
+                    continue
+                errors = cap_errors(true_acc, estim_acc)
+                table.add(dataset, method, errors)
 
-        tex = table.latexTabular()
-        table_name = f'{basedir}_{classifier}_{metric}.tex'
-        with open(f'./tables/{table_name}', 'wt') as foo:
-            foo.write('\\resizebox{\\textwidth}{!}{%\n')
-            foo.write('\\begin{tabular}{c|'+('c'*len(methods))+'}\n')
-            foo.write(tex)
-            foo.write('\\end{tabular}%\n')
-            foo.write('}\n')
+            tex = table.latexTabular()
+            table_name = f'{basedir}_{classifier}_{metric}.tex'
+            with open(f'./tables/{table_name}', 'wt') as foo:
+                foo.write('\\begin{table}[h]\n')
+                foo.write('\\centering\n')
+                foo.write('\\resizebox{\\textwidth}{!}{%\n')
+                foo.write('\\begin{tabular}{c|'+('c'*len(methods))+'}\n')
+                foo.write(tex)
+                foo.write('\\end{tabular}%\n')
+                foo.write('}\n')
+                foo.write('\\caption{Classifier ' + classifier.replace('_', ' ') + ('(oracle)' if with_oracle else '') +
+                          ' evaluated in terms of ' + metric.replace('_', ' ') + '}\n')
+                foo.write('\\end{table}\n')
 
-        tex_doc += "\input{" + table_name + "}\n\n"
+            tex_doc += "\input{" + table_name + "}\n\n"
 
     tex_doc += """
     \\end{document}
@@ -368,3 +381,4 @@ def gen_tables(basedir, datasets):
     os.system('pdflatex main.tex')
     os.system('rm main.aux main.log')
 
+