{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003988832,0.0001147889,0.0002187327,0.0001124027,0.0001605358,0.0001999988,0.00005299802,0.00005558565,0.000005463928],"category_scores_gemma":[0.0003945804,0.000112662,0.00002263601,0.0001847288,0.00004524959,0.0005672125,0.00002953057,0.0001254718,5.422345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004950242,"about_ca_system_score_gemma":0.0001000144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.508268e-7,"about_ca_topic_score_gemma":2.469666e-7,"domain_scores_codex":[0.9987631,0.0001603925,0.0004526985,0.0002335505,0.0002457251,0.0001444815],"domain_scores_gemma":[0.9967427,0.001897899,0.0002360699,0.00004466731,0.0009463384,0.0001323711],"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":[0.00007571016,0.00004524665,0.00002491133,0.00002242677,0.00001607514,0.000008314832,0.0001909819,0.9392797,0.00005442782,0.02869899,0.00002320838,0.03155999],"study_design_scores_gemma":[0.001092998,0.0002438546,0.0003972474,0.0000148775,0.00002288807,0.0001042415,0.00003689525,0.9673978,0.0001129297,0.03040934,0.00005063126,0.0001162911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001200371,0.00006481707,0.9982799,0.0001125982,0.0001071639,0.000189399,0.00001494581,0.00002391224,0.000006882773],"genre_scores_gemma":[0.2914165,0.00001746406,0.7084489,0.00005232846,0.00003330477,0.000002222224,0.00001589802,0.000007607442,0.000005726833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2902161,"threshold_uncertainty_score":0.4594223,"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."}}