The initial dataset (see 4.1.1) was processed as follows. First, pressure traces were extracted and treated as time-series data. Experiments with traces exceeding 10,000 time steps, shorter than 100 time steps, or longer than 75 minutes were discarded, yielding a final set of 25,036 experiments. Next, we featurized the remaining experiments using tsfresh default set of 783 features. Features that contained undefined values in any experiment were removed, reducing the feature set to 585. We then processed these features using Scikit-learn : (1) each feature was scaled using a Min-MaxScaler, (2) features with a variance of less than 0.01 were filtered out-resulting in 122 features, and (3) a pairwise Spearman correlation matrix was computed; for each pair of features with an absolute correlation greater than 0.9, only one was retained. This procedure resulted in a final processed dataset comprising 99 features suitable for classical machine learning modeling.