Since the classification task is sensitive to class imbalance, and due to usage of SNA framework we evaluated three class ratio variants: 1:1, 1:10, 1:100. The positive class was represented by the 93 initially annotated as affected by air bubble contamination, The model's hyperparameters (and architecture) were fixed for Round 2 and later optimized for deployment using Optuna by maximizing the validation F1 score with an optimization budget of 2000 trials.