{"id":"W2909094277","doi":"10.1186/s12862-018-1326-7","title":"Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates","year":2019,"lang":"en","type":"article","venue":"BMC Evolutionary Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Tula Foundation","keywords":"Nonsynonymous substitution; Biology; Inference; Positive selection; Selection (genetic algorithm); Entomology; Genetics; Animal ecology; Evolutionary biology; Computational biology; Ecology; Gene; Machine learning; Computer science; Genome; 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":[0.01466091,0.0006930121,0.001108191,0.0009896043,0.000531872,0.00187085,0.002394288,0.001543622,0.001738168],"category_scores_gemma":[0.05584777,0.0008049215,0.001786218,0.0009072142,0.001808632,0.001902698,0.001688269,0.002434239,0.0002341749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010237,"about_ca_system_score_gemma":0.001271463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006403396,"about_ca_topic_score_gemma":0.004943586,"domain_scores_codex":[0.9927346,0.004892576,0.0002652275,0.001407291,0.0004307034,0.0002696772],"domain_scores_gemma":[0.9407318,0.05238523,0.002931771,0.002544023,0.0009228657,0.0004843513],"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":[0.0003050375,0.00009072466,0.03125019,0.0001247121,0.0004027228,0.0004283378,0.0003081613,0.9116101,0.003280769,0.01875997,0.0003963482,0.03304302],"study_design_scores_gemma":[0.0000159106,0.00004890693,0.002132464,0.000009053761,0.00002792944,0.00007568738,0.00001814875,0.9810709,0.0004452965,0.01599479,0.000144253,0.00001668981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3263243,0.0001784032,0.6711305,0.0002973441,0.0000238456,0.00005237641,0.000223385,0.000571065,0.001198767],"genre_scores_gemma":[0.9248684,0.0000559939,0.07391354,0.000133969,0.00002859367,0.00006039506,0.0003329707,0.00009464884,0.0005116161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01466091,"threshold_uncertainty_score":0.07753521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957578518072501,"score_gpt":0.2549853873225569,"score_spread":0.2354096021418319,"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."}}