{"id":"W3000355524","doi":"10.1016/j.idm.2019.12.010","title":"A primer on model selection using the Akaike Information Criterion","year":2020,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":487,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Selection (genetic algorithm); Bayesian information criterion; Model selection; Workflow; Minimum description length; Computer science; Calibration; Computation; Data collection; Mathematical model; Information Criteria; Data mining; Statistics; Machine learning; Mathematics; Artificial intelligence; Algorithm","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":[0.00008355145,0.0001460626,0.00009229527,0.00004340381,0.0002325408,0.00006434794,0.00008896763,0.00006983159,0.000006107746],"category_scores_gemma":[0.00002143347,0.0001245185,0.0001370896,0.000177361,0.00002506375,0.00001844089,0.00004330267,0.00009428976,0.0000126412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003365802,"about_ca_system_score_gemma":0.00009258569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001576483,"about_ca_topic_score_gemma":0.000002509777,"domain_scores_codex":[0.999205,0.00005316379,0.0001872796,0.0002164831,0.000162501,0.0001756057],"domain_scores_gemma":[0.9994705,0.000004570372,0.00008839043,0.0001930503,0.0001032757,0.0001401863],"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":[0.0000872731,0.00001926887,0.0007630851,0.00001312337,0.00004783139,2.376697e-7,0.00009031835,0.9883853,0.009959781,0.00005629966,0.0001622308,0.0004152884],"study_design_scores_gemma":[0.0001715126,0.00005299615,0.00003905146,0.000007662635,0.00008219457,0.00000164126,0.0000110067,0.9946501,0.003428063,0.0001585025,0.001250911,0.0001463219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5026416,0.00008835652,0.4969241,0.00009894432,0.00002815831,0.0001059215,0.000003658587,0.00002556751,0.00008370355],"genre_scores_gemma":[0.9975952,0.0000437926,0.0005570901,0.00142072,0.0002782762,0.00001567324,0.00005645807,0.0000193159,0.0000134763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.496367,"threshold_uncertainty_score":0.5077717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207519264189321,"score_gpt":0.2422796719396598,"score_spread":0.2202044792977666,"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."}}