{"id":"W7056804350","doi":"","title":"Guiding Expert Database Tuning with Explainable AI","year":2025,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Thermal properties of materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"York University","keywords":"IBM; Workload; Expert system; Benchmark (surveying); Domain (mathematical analysis); SQL; Data manipulation language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001853325,0.001038075,0.0004408989,0.0006756631,0.0003642763,0.001376734,0.001343448,0.0009160322,0.002094888],"category_scores_gemma":[0.01166162,0.0004865181,0.0004043115,0.0003159443,0.0007548824,0.00161196,0.001627986,0.001290523,0.0003554678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009514268,"about_ca_system_score_gemma":0.0009058313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003934856,"about_ca_topic_score_gemma":0.005369267,"domain_scores_codex":[0.9989649,0.0004404832,0.00004410378,0.0002268663,0.0002483097,0.00007539057],"domain_scores_gemma":[0.9958034,0.00290297,0.000329166,0.0005169205,0.0003471706,0.0001003225],"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":[0.0003909415,0.0002735931,0.005515754,0.0002174102,0.00008871138,0.0002069095,0.001179042,0.7586166,0.02860496,0.01786422,0.00514011,0.1819018],"study_design_scores_gemma":[0.00001119085,0.0000229525,0.0002237673,0.000004594038,0.00000463401,0.00001094376,0.00002740581,0.9908519,0.002492462,0.005710581,0.0006333893,0.000006161971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08186726,0.0001676214,0.9070777,0.0007004958,0.00002902536,0.0001292933,0.0001766465,0.005988425,0.003863541],"genre_scores_gemma":[0.7335439,0.00009144226,0.2639908,0.0002105776,0.00002308819,0.0001211924,0.0002436075,0.000381569,0.001393914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003934856,"threshold_uncertainty_score":0.009801447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645789177396149,"score_gpt":0.1713219273581501,"score_spread":0.1548640355841886,"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."}}