{"id":"W4416046438","doi":"10.48550/arxiv.2505.21813","title":"Optimizing Data Augmentation through Bayesian Model Selection","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Robustness (evolution); Model selection; Generalization; Probabilistic logic; Bayesian probability; Bayesian optimization; Marginal likelihood; Selection (genetic algorithm)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008359482,0.001871621,0.002387669,0.001351172,0.0008592685,0.002277254,0.003287802,0.002801509,0.002065863],"category_scores_gemma":[0.02235831,0.001645039,0.00193084,0.001452454,0.002800264,0.003746827,0.0050641,0.005036807,0.001230917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592087,"about_ca_system_score_gemma":0.002856783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00300522,"about_ca_topic_score_gemma":0.003674836,"domain_scores_codex":[0.9948446,0.002868497,0.0002149915,0.0008660269,0.0009731256,0.0002328247],"domain_scores_gemma":[0.9917653,0.005413777,0.0006063617,0.001282918,0.0007187235,0.0002129119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001779871,0.000125759,0.001229605,0.0002153094,0.0001286682,0.00009402924,0.000192108,0.8088685,0.004482842,0.06050532,0.004003902,0.119976],"study_design_scores_gemma":[0.0000142491,0.00002100375,0.00007668533,0.00002194751,0.000008450552,0.00002219568,0.000009499462,0.9682147,0.0008582724,0.02990066,0.0008409752,0.00001132403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002748625,0.0001457325,0.9957659,0.0002522505,0.00001609816,0.00003706172,0.00004760244,0.0004569288,0.0005298505],"genre_scores_gemma":[0.2934536,0.0005204506,0.7006133,0.000851971,0.0001595803,0.0006557908,0.0007555402,0.0005191786,0.002470613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008359482,"threshold_uncertainty_score":0.04420966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1231120383278702,"score_gpt":0.3565369785081619,"score_spread":0.2334249401802917,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}