{"id":"W4409348651","doi":"10.1609/aaai.v39i27.35103","title":"Automated, Interpretable, and Scalable Scientific Machine Learning","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Machine learning; Artificial intelligence; Data science; Operating system","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":[0.003496474,0.001382386,0.001171619,0.001728306,0.001050682,0.003861021,0.004227519,0.001607616,0.00596316],"category_scores_gemma":[0.01642873,0.0009233825,0.001440004,0.00218053,0.00164196,0.006539358,0.00353569,0.00305661,0.004042892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809483,"about_ca_system_score_gemma":0.004333158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002820021,"about_ca_topic_score_gemma":0.005400081,"domain_scores_codex":[0.9965709,0.0008388159,0.0002286427,0.0005597246,0.001614802,0.0001869408],"domain_scores_gemma":[0.9912181,0.002851715,0.0004904622,0.00359426,0.001651283,0.0001940333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002736378,0.0003765702,0.002113096,0.0006659256,0.0001784718,0.0003171477,0.0002687525,0.2340667,0.02153317,0.1800459,0.05990687,0.5002537],"study_design_scores_gemma":[0.0000427606,0.00002504123,0.0002096716,0.00002806928,0.00002002429,0.00004361504,0.00005252394,0.7565253,0.006566358,0.2218235,0.01464485,0.0000182895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005905221,0.0005234582,0.9669487,0.002061576,0.0001442513,0.0001616202,0.001312056,0.0164744,0.006468639],"genre_scores_gemma":[0.1226742,0.0007966911,0.8669294,0.0003871073,0.000264067,0.0003449862,0.004430198,0.001354754,0.002818526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00596316,"threshold_uncertainty_score":0.01994872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189146350175846,"score_gpt":0.3751937663386241,"score_spread":0.2562791313210395,"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."}}