{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01123705,0.002814966,0.002394202,0.004290951,0.0009249251,0.003756315,0.004258945,0.003900954,0.009451912],"category_scores_gemma":[0.02473307,0.001640449,0.003511185,0.004490628,0.003072303,0.006199297,0.002938775,0.009996851,0.005876537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001585935,"about_ca_system_score_gemma":0.001657404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776277,"about_ca_topic_score_gemma":0.001556476,"domain_scores_codex":[0.992748,0.004690458,0.0005557187,0.0006856037,0.001196895,0.0001233351],"domain_scores_gemma":[0.9846514,0.01285388,0.0005016875,0.0007710278,0.001083813,0.000138247],"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":[0.00007057135,0.0001467368,0.0008644888,0.001390027,0.0002916021,0.0006465535,0.0004787113,0.05184537,0.002221533,0.6839254,0.049841,0.208278],"study_design_scores_gemma":[0.00002003433,0.00008374288,0.0003441795,0.0005661153,0.00003839447,0.0003002262,0.00008496427,0.07587575,0.001290519,0.816447,0.1048121,0.0001369057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001551277,0.005695027,0.9894097,0.001574241,0.0004949601,0.00006543989,0.0001503697,0.000325789,0.002129295],"genre_scores_gemma":[0.009410498,0.01516607,0.9669642,0.001963806,0.001746714,0.0005691549,0.0004893044,0.0005918946,0.003098324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01123705,"threshold_uncertainty_score":0.05942792,"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."}}