To observe how uncertainty measures respond to variations in data coverage and noise levels, we conducted two studies focusing on halogen atoms. In the first experiment, we explored how the absence of specific training examples affects uncertainty. We trained a model on a subset of data that excluded molecules containing halogen atoms, creating a "halogen-agnostic" model. This setup allowed us to primarily assess epistemic uncertainty, which typically increases when relevant examples are missing from the training set. In the second experiment, we focused on the impact of label noise. We trained a model using a data set in which 20% of the halogen atoms were deliberately mislabeled as positive. This "noisy halogens" model enabled us to investigate how aleatoric uncertainty, which is influenced by noisy training data, changes under these conditions. We compared both models against a "baseline" version of AWESOM trained on the original data.