{"id":"W4379801796","doi":"10.1002/cjs.11775","title":"Objective model selection with parallel genetic algorithms using an eradication strategy","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Model selection; Machine learning; Genetic algorithm; Artificial intelligence; Population; Algorithm; Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0001348205,0.00008677004,0.0001002698,0.0002672047,0.0002927129,0.0001128944,0.0002868012,0.00003751926,0.000005809525],"category_scores_gemma":[0.00001334127,0.000083018,0.00001656595,0.000626815,0.00005842396,0.0003856159,0.00000702552,0.0001443052,0.000005787978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935646,"about_ca_system_score_gemma":0.002411412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001390586,"about_ca_topic_score_gemma":0.003949353,"domain_scores_codex":[0.9991853,0.00003357993,0.0002245269,0.000146843,0.0001806984,0.0002290294],"domain_scores_gemma":[0.9988416,0.00002926885,0.0001759611,0.0001375456,0.000424146,0.0003914143],"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":[0.000001843007,0.00001673754,0.0004211481,0.000003667026,0.00002076497,0.00005284276,0.0004500056,0.9517,0.00008063653,0.03334288,0.0008869841,0.01302243],"study_design_scores_gemma":[0.0001509504,0.0001682792,0.01639108,0.000009456115,0.00001458076,0.0002464805,0.00008378398,0.9556459,0.00001113016,0.02708669,0.00009022001,0.0001014843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01528742,0.00004034454,0.9842581,0.0001397903,0.00006171226,0.00007936451,0.00007154774,0.00001816447,0.00004351438],"genre_scores_gemma":[0.2935595,0.00001636455,0.706227,0.00003448249,0.00008797512,0.000003323974,0.00001117785,0.000009307283,0.00005091792],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.278272,"threshold_uncertainty_score":0.4277741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842926554301982,"score_gpt":0.2630375572372838,"score_spread":0.224608291694264,"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."}}