{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01580269,0.0009772344,0.001346346,0.001037823,0.001408382,0.002355342,0.001915117,0.001621948,0.002913544],"category_scores_gemma":[0.08284996,0.0005569232,0.001357898,0.001839748,0.003383841,0.002497129,0.001751653,0.002729008,0.0005244829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008871158,"about_ca_system_score_gemma":0.001297158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002537058,"about_ca_topic_score_gemma":0.003194393,"domain_scores_codex":[0.9855971,0.008689166,0.0008576822,0.00271071,0.001441563,0.0007038677],"domain_scores_gemma":[0.937315,0.05081597,0.004440654,0.004714653,0.001807693,0.0009060398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001513803,0.0003552734,0.6854123,0.001854667,0.002654872,0.0009879795,0.002013132,0.09540154,0.07106616,0.04225748,0.004410241,0.09207266],"study_design_scores_gemma":[0.0002253828,0.001303451,0.2976597,0.0002616766,0.0009581011,0.001788152,0.001400492,0.5164782,0.0500715,0.1206312,0.00892015,0.0003019785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8059515,0.0007589263,0.1853864,0.001002435,0.0002602137,0.0001678877,0.000914685,0.0009648813,0.004593184],"genre_scores_gemma":[0.9857526,0.00006035469,0.01306389,0.0002546641,0.00002270894,0.00007428817,0.0004511698,0.000191573,0.0001288412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01580269,"threshold_uncertainty_score":0.08357364,"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."}}