{"id":"W6964077481","doi":"10.24433/co.2314903.v2","title":"Dynamic metaheuristic selection via Thompson Sampling for online optimization","year":2024,"lang":"en","type":"other","venue":"Code Ocean","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canetique (Canada)","funders":"","keywords":"Metaheuristic; Selection (genetic algorithm); Benchmark (surveying); Sampling (signal processing); Range (aeronautics); Function (biology); Optimization problem","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002534222,0.0005123174,0.0005261147,0.0008185583,0.00006770821,0.0001142845,0.0002386426,0.000422623,0.0009619721],"category_scores_gemma":[0.0001624059,0.0005313643,0.0002061258,0.0005878779,0.00004525579,0.00005449017,0.00005404633,0.0003499362,0.001580189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004249231,"about_ca_system_score_gemma":0.0000857559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003175904,"about_ca_topic_score_gemma":0.0005942049,"domain_scores_codex":[0.9980453,0.00006181899,0.0003751909,0.0008278549,0.0002791892,0.0004106311],"domain_scores_gemma":[0.9990017,0.00006216979,0.0003490699,0.0003753059,0.0001162146,0.00009550951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008485222,0.0001880528,0.00000809368,0.001490961,0.000626319,0.000004870425,0.00005392008,0.1005233,0.000456603,0.0002853517,0.8942578,0.002019904],"study_design_scores_gemma":[0.0002926061,0.00005585988,0.000003497763,0.0004220837,0.0007312733,0.00001397681,0.000007272733,0.6350543,0.000009959391,0.0004824091,0.3625004,0.0004264274],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001271466,0.003121097,0.9480664,0.00007434525,0.001715347,0.001594276,0.005366149,0.003199606,0.03685009],"genre_scores_gemma":[0.0006086197,0.00009956125,0.2639779,0.00004341691,0.0008973862,0.00003428853,0.008564021,0.006292393,0.7194824],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6840885,"threshold_uncertainty_score":0.9999513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03035339702112283,"score_gpt":0.3270189577265402,"score_spread":0.2966655607054174,"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."}}