{"id":"W2803714548","doi":"10.1093/molbev/msy047","title":"Multiple Factors Confounding Phylogenetic Detection of Selection on Codon Usage","year":2018,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Compute Canada; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Biology; False positive paradox; Selection (genetic algorithm); Robustness (evolution); Genetics; Mutation rate; Computational biology; Mutation; Population; Negative selection; Phylogenetic tree; Codon usage bias; CpG site; Statistical hypothesis testing; Gene; Statistics; Computer science; Machine learning; Mathematics; DNA methylation; Genome","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":[0.0001213673,0.0001369981,0.0001306333,0.00007018715,0.0001570119,0.000005001213,0.00005856131,0.0001858021,0.000003969602],"category_scores_gemma":[0.00009151747,0.000128168,0.00005180563,0.00008141837,0.0002257443,7.131983e-7,0.00005012675,0.00005315672,0.00000270902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001742894,"about_ca_system_score_gemma":0.00001905443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006735658,"about_ca_topic_score_gemma":0.0001387595,"domain_scores_codex":[0.9992307,0.00008996586,0.0001550327,0.0002989759,0.00004457647,0.0001807376],"domain_scores_gemma":[0.9996347,0.00001607498,0.00008849728,0.0001320914,0.00009107609,0.00003760276],"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":[0.0000947061,0.00002134528,0.09337412,0.000005913899,0.00005202331,1.100171e-7,0.00003608243,0.00002542053,0.905234,0.0003692929,0.000006940497,0.000780029],"study_design_scores_gemma":[0.000318711,0.001382646,0.1890533,0.000005346942,0.0000207325,0.000004933886,0.00004278917,0.000204306,0.807496,0.0005390257,0.0008108207,0.0001214221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712428,0.000604709,0.02751367,0.00001083597,0.0002241944,0.0001364002,0.00001164837,0.000004910359,0.0002508596],"genre_scores_gemma":[0.999464,0.00006845999,0.0002257993,0.00004278486,0.0001238981,0.000008960222,0.00002436287,0.00001159431,0.00003019231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09773807,"threshold_uncertainty_score":0.5226538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009603388649460467,"score_gpt":0.2520671125770969,"score_spread":0.2424637239276364,"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."}}