{"id":"W2987894624","doi":"10.1093/molbev/msz228","title":"On the Use of Information Criteria for Model Selection in Phylogenetics","year":2019,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Bayesian information criterion; Model selection; Information Criteria; Bayes factor; Divergence (linguistics); Bayes' theorem; Selection (genetic algorithm); Statistics; Bayesian probability; Biology; Mathematics; Computer science; Econometrics; Artificial intelligence","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":[0.0001175737,0.00007488175,0.00008123426,0.00004311099,0.00003441826,0.000005833576,0.00004574352,0.0001125042,0.000001180677],"category_scores_gemma":[0.00007194989,0.00005906074,0.00003375636,0.00004364393,0.00004656723,0.000001490616,0.00003420385,0.00003344374,9.910565e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008213709,"about_ca_system_score_gemma":0.00002040979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001307748,"about_ca_topic_score_gemma":0.00001172047,"domain_scores_codex":[0.9995699,0.00004211197,0.0001348111,0.0001205252,0.00002430553,0.0001083731],"domain_scores_gemma":[0.9997597,0.00002194627,0.00005602534,0.00009315633,0.00005793561,0.00001118915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001272364,0.00001415991,0.009799794,0.00001164774,0.00002018958,1.147876e-8,0.00002935041,0.01078824,0.9583784,0.02040389,0.0001197093,0.0003074212],"study_design_scores_gemma":[0.002901315,0.003504335,0.1618772,0.00004582292,0.00006742873,0.000009619203,0.0001271783,0.2883092,0.4670569,0.06624608,0.009177744,0.0006771471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370872,0.0001711346,0.06223354,0.00009522311,0.00004903314,0.000289217,0.00002768211,0.000001029018,0.00004591709],"genre_scores_gemma":[0.9975379,0.00005785951,0.002056687,0.0002455717,0.0000112312,0.00003052938,0.00003650604,0.000004479414,0.00001921177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4913214,"threshold_uncertainty_score":0.2408427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584992803859321,"score_gpt":0.2543346152213883,"score_spread":0.2384846871827951,"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."}}