{"id":"W3036039277","doi":"10.1093/molbev/msaa151","title":"Consequences of Stability-Induced Epistasis for Substitution Rates","year":2020,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Epistasis; Biology; Stability (learning theory); Selection (genetic algorithm); Inference; Evolutionary biology; Molecular evolution; Substitution (logic); Mutation; Genetics; Computer science; Gene; Artificial intelligence; Phylogenetics; Machine learning","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":[0.003129902,0.0005191012,0.0005248975,0.0005940823,0.0004490378,0.0007543773,0.0009415533,0.0009405002,0.0009137026],"category_scores_gemma":[0.01880669,0.0003940375,0.001120824,0.0004515959,0.001305541,0.001483244,0.0008010768,0.00120013,0.0001353753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029053,"about_ca_system_score_gemma":0.0005929134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003294023,"about_ca_topic_score_gemma":0.002980975,"domain_scores_codex":[0.99806,0.0009341757,0.0001292749,0.0004538981,0.000261064,0.0001615142],"domain_scores_gemma":[0.9939325,0.004150558,0.0006730894,0.00081406,0.0002267301,0.0002029022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003544828,0.0001533624,0.05912604,0.0001614074,0.0003891491,0.0009412169,0.0004796253,0.7459173,0.08719724,0.08585028,0.000751476,0.01867837],"study_design_scores_gemma":[0.00002441225,0.0001159388,0.01218759,0.000006993012,0.00005132816,0.0002201776,0.00005811602,0.9394944,0.005521185,0.04204897,0.0002299257,0.00004103082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8680267,0.000133739,0.1286102,0.0006923927,0.00002458466,0.00003092211,0.0001766721,0.0002562418,0.002048473],"genre_scores_gemma":[0.9952067,0.00003515092,0.004515173,0.00004575891,0.000004106219,0.00001543304,0.00004094845,0.00001942652,0.0001172459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003294023,"threshold_uncertainty_score":0.01655269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061034607182653,"score_gpt":0.2928407493185667,"score_spread":0.2722304032467402,"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."}}