diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py
index 5f570b94cfed4d61f71bb2662183295df81f4978..1f1123e7fdf98c4a55066351cbb13b8a9b6162ff 100644
--- a/pes_to_spec/__init__.py
+++ b/pes_to_spec/__init__.py
@@ -2,4 +2,4 @@
 Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements.
 """
 
-VERSION = "0.2.2"
+VERSION = "0.2.3"
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index d04881b96351fa87407b546f54686c2eb0363644..695dc689a0de450137035c752e2fd207dff01b73 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -645,18 +645,6 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
         y_std = np.sqrt(sigmas_squared_data + self.fast_inv_alpha)
         return y, y_std
 
-        #n_jobs = self.n_jobs
-        #y = Parallel(n_jobs=n_jobs, prefer="threads")(
-        #    delayed(e.predict)(X, return_std) for e in self.estimators_
-        #    #delayed(e.predict)(X) for e in self.estimators_
-        #)
-        #if return_std:
-        #    y, unc = zip(*y)
-        #    return np.asarray(y).T, np.asarray(unc).T
-
-        #return np.asarray(y).T
-
-
 class UncorrelatedDeviation(OutlierMixin, BaseEstimator):
     """
     Detect outliers from uncorrelated inputs.
@@ -1055,12 +1043,8 @@ class Model(TransformerMixin, BaseEstimator):
         def is_inlier(in_data, ch: str) -> np.ndarray:
             data_pca = self.channel_pca[ch].transform(in_data)
             return self.ood[ch].predict(data_pca)
-
-        #result = Parallel(n_jobs=-1)(
-        #    delayed(is_inlier)(low_res_selected[ch], ch) for ch in channels
-        #)
-        #result = dict(result)
-        return {ch: is_inlier(low_res_selected[ch], ch) for ch in channels}
+        result = {ch: is_inlier(low_res_selected[ch], ch) for ch in channels}
+        return result
 
     def check_compatibility(self, low_res_data: Dict[str, np.ndarray]) -> np.ndarray:
         """