{"id":"W3092451968","doi":"10.1093/molbev/msaa265","title":"A Bayesian Mutation–Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes","year":2020,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Biology; Bayesian probability; Selection (genetic algorithm); Gene; Computational biology; Adaptive evolution; Mutation; Coding (social sciences); Genetics; Bayes' theorem; Coding region; Machine learning; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009787831,0.0008099828,0.001147828,0.001732104,0.0007599529,0.001138229,0.001914431,0.001114858,0.0006599143],"category_scores_gemma":[0.01518465,0.0005115974,0.0009858895,0.0009835286,0.002092441,0.001237516,0.001243584,0.001314964,0.0001774067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369575,"about_ca_system_score_gemma":0.001686248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00581216,"about_ca_topic_score_gemma":0.004803571,"domain_scores_codex":[0.9964448,0.00233041,0.0001248923,0.0005237953,0.0004445085,0.0001317161],"domain_scores_gemma":[0.9947702,0.003947456,0.0004385478,0.0002970614,0.0003454437,0.0002012916],"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":[0.0002568831,0.0001207547,0.009028543,0.0001591522,0.0003019064,0.000204297,0.0001774642,0.7276132,0.02187436,0.1296048,0.0007141709,0.1099445],"study_design_scores_gemma":[0.00002422041,0.00004293908,0.001322206,0.00001089609,0.00001751158,0.00007093631,0.00001219978,0.9463531,0.0009694248,0.05073085,0.0004124557,0.00003332209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0204576,0.0002473714,0.9785751,0.0001300184,0.00001220254,0.00004041706,0.00004184501,0.0001496174,0.0003458563],"genre_scores_gemma":[0.4643841,0.0002926594,0.533939,0.0001774735,0.00006891979,0.0001895205,0.0001833081,0.00009442399,0.0006705929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009787831,"threshold_uncertainty_score":0.05176359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318354157375148,"score_gpt":0.2468234270820534,"score_spread":0.2336398855083019,"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."}}