{"id":"W4206177129","doi":"10.1002/047147326x.ch5","title":"Model Selection and Validation","year":2003,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Model selection; Weibull distribution; Goodness of fit; Selection (genetic algorithm); Computer science; Set (abstract data type); Model validation; Data mining; Focus (optics); Data set; Machine learning; Artificial intelligence; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007374056,0.0001437994,0.0002393929,0.0001675404,0.00007826654,0.000127062,0.0001014005,0.0001961839,0.0004333263],"category_scores_gemma":[0.000852789,0.0001280029,0.00001367446,0.000276237,0.0001776402,0.0001016503,0.00004636062,0.000132639,0.000006505619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002902174,"about_ca_system_score_gemma":0.0000542468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005408056,"about_ca_topic_score_gemma":0.0006782435,"domain_scores_codex":[0.99862,0.0001143081,0.0004089814,0.0004428719,0.000298833,0.0001149687],"domain_scores_gemma":[0.9991488,0.0002393944,0.0001829962,0.0002751219,0.0001035043,0.0000501086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002057288,0.00008023429,0.007873415,0.00009440777,0.000008380221,5.328174e-7,0.00026998,0.001120907,0.00001059365,0.6664855,0.2646686,0.05936686],"study_design_scores_gemma":[0.00009319497,0.00003136432,0.0002547504,0.00003259923,0.00000778985,0.000003186961,0.0000272288,0.03272684,0.00001462309,0.8078014,0.1588593,0.0001477375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006845425,0.0003554727,0.918154,0.000216507,0.00009817099,0.001012224,0.0008794505,0.0001415666,0.07845806],"genre_scores_gemma":[0.00726877,0.001471245,0.7293344,0.0001073693,0.00004461185,0.00014962,0.0001069509,0.0001057659,0.2614113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1888196,"threshold_uncertainty_score":0.5219809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08871937579510207,"score_gpt":0.3700308270345239,"score_spread":0.2813114512394218,"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."}}