{"id":"W1974884708","doi":"10.1007/s10928-006-9004-6","title":"A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection","year":2006,"lang":"en","type":"article","venue":"Journal of Pharmacokinetics and Pharmacodynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Computer science; Covariate; Residual; Genetic algorithm; Identification (biology); Model selection; Selection (genetic algorithm); Machine learning; Algorithm; Artificial intelligence; Linear model; Mathematical optimization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002196534,0.001267807,0.002209069,0.001568886,0.0007965277,0.001327017,0.002569159,0.002288475,0.002045298],"category_scores_gemma":[0.005465383,0.0007693513,0.001266156,0.00152328,0.0006896628,0.000984104,0.001231762,0.001888913,0.000529371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000930278,"about_ca_system_score_gemma":0.001368781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009691291,"about_ca_topic_score_gemma":0.009295871,"domain_scores_codex":[0.9989812,0.0005322594,0.00005246838,0.0001217706,0.0002547078,0.00005755351],"domain_scores_gemma":[0.9974834,0.001846469,0.0001025164,0.00009102461,0.0004176739,0.00005890233],"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":[0.00005259287,0.00006677789,0.0004103728,0.00003457325,0.0001551036,0.00006069651,0.00002866644,0.9463226,0.000659325,0.00281943,0.0006094719,0.04878038],"study_design_scores_gemma":[0.00000968839,0.00001704104,0.00004401937,0.000002185868,0.00001164128,0.000009567138,0.000002139805,0.9989231,0.00009311066,0.000780579,0.0001033827,0.000003571588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01019083,0.0002630864,0.9870918,0.0001756639,0.00006111884,0.00008397278,0.00004475325,0.0004760073,0.001612761],"genre_scores_gemma":[0.3657971,0.0003057749,0.6291045,0.0003646006,0.0001314717,0.0005627589,0.000252184,0.0002093534,0.003272162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009691291,"threshold_uncertainty_score":0.01926976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001523600931729,"score_gpt":0.2508635142443035,"score_spread":0.2408482782349863,"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."}}