{"id":"W3139526846","doi":"10.1080/00949655.2021.1900182","title":"Approximately optimal subset selection for statistical design and modelling","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Mathematics; Mathematical optimization; Selection (genetic algorithm); Cross-entropy method; Entropy (arrow of time); Optimization problem; Set (abstract data type); Optimal design; Principle of maximum entropy; Algorithm; Applied mathematics; Statistics; Computer science; Artificial intelligence; Quadratic assignment problem","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":[0.01484393,0.002305164,0.002909153,0.001710056,0.0006533478,0.001652661,0.001790296,0.00187406,0.00261284],"category_scores_gemma":[0.04220612,0.00127686,0.001627613,0.001926222,0.002918781,0.002020663,0.002314072,0.002232341,0.0006464795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001811932,"about_ca_system_score_gemma":0.002690294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547059,"about_ca_topic_score_gemma":0.001239033,"domain_scores_codex":[0.9878405,0.009867636,0.0002667319,0.0008134401,0.000979221,0.0002325862],"domain_scores_gemma":[0.9633958,0.03213489,0.001320893,0.00166392,0.001182243,0.0003022987],"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":[0.0001309537,0.00005967262,0.0007219469,0.0001776416,0.0001766201,0.00009790915,0.0000965014,0.8429937,0.001097575,0.1226795,0.001006578,0.03076134],"study_design_scores_gemma":[0.00003351538,0.00006032751,0.00008516488,0.00001678396,0.0000136013,0.00001932189,0.000008601168,0.9248893,0.0004349868,0.07368854,0.0007400996,0.000009691418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001559036,0.0001162001,0.9978884,0.00008525888,0.00001019599,0.0000455989,0.00002189742,0.00005078606,0.0002226765],"genre_scores_gemma":[0.180318,0.0007403679,0.8152733,0.0002229827,0.0001414255,0.00133382,0.0003677343,0.0001497345,0.001452595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01484393,"threshold_uncertainty_score":0.07850313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04670705401099513,"score_gpt":0.3296597938246988,"score_spread":0.2829527398137037,"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."}}