{"id":"W4242172665","doi":"10.3410/f.13492992.14871096","title":"Faculty Opinions recommendation of A population genetics-phylogenetics approach to inferring natural selection in coding sequences.","year":2012,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Natural selection; Selection (genetic algorithm); Coding (social sciences); Evolutionary biology; Phylogenetics; Population genetics; Population; Biology; Computational biology; Genetics; Computer science; Artificial intelligence; Gene; Demography; Sociology; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002343142,0.002139694,0.001890474,0.003953645,0.0007495997,0.0026501,0.003805902,0.002542125,0.08517492],"category_scores_gemma":[0.01317553,0.0009600601,0.0017344,0.005585996,0.0004119215,0.001471897,0.001869066,0.002512732,0.07376234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001362441,"about_ca_system_score_gemma":0.002861249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01808151,"about_ca_topic_score_gemma":0.05517413,"domain_scores_codex":[0.9987948,0.0002019958,0.0001069953,0.0004235663,0.0003374646,0.0001351926],"domain_scores_gemma":[0.9957188,0.001437854,0.0003513024,0.001138147,0.0007520394,0.0006017578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008306746,0.00003355567,0.001575605,0.000548547,0.00008019121,0.00002279355,0.00001486441,0.0004307345,0.0001993166,0.0003743942,0.9930988,0.003538008],"study_design_scores_gemma":[0.0006928583,0.00003895059,0.01055933,0.0003266087,0.0001703539,0.0001147519,0.0000544888,0.003311611,0.001026137,0.003253695,0.9804011,0.00005007837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002427605,0.00006346445,0.0003810575,0.0001219156,0.00003500521,0.00001723877,0.997318,0.001021667,0.0007989373],"genre_scores_gemma":[0.0006989196,0.00005284446,0.001104301,0.00009207066,0.00001097571,0.00005952584,0.9969807,0.0001365176,0.0008639893],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08517492,"threshold_uncertainty_score":0.2849385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332296225976709,"score_gpt":0.3662043624931036,"score_spread":0.3228814002333366,"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."}}