{"id":"W4206728270","doi":"10.1093/gbe/evac004","title":"Fitness Effects of Mutations: An Assessment of PROVEAN Predictions Using Mutation Accumulation Data","year":2022,"lang":"en","type":"article","venue":"Genome Biology and Evolution","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biology; Mutation; Mutant; Genetics; Leverage (statistics); Mutation rate; Human genetics; Neutral mutation; Genome; Fitness landscape; Evolutionary biology; Computational biology; Gene; Population; Machine learning; Computer science","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":[0.01468193,0.001452247,0.001024815,0.004581736,0.001008827,0.001523639,0.001145615,0.0009664586,0.0009128612],"category_scores_gemma":[0.03485115,0.0003107761,0.001113681,0.001877477,0.0008157856,0.002450883,0.001510586,0.001285171,0.0004770579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054095,"about_ca_system_score_gemma":0.0007797609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003192814,"about_ca_topic_score_gemma":0.004950118,"domain_scores_codex":[0.9965187,0.001589759,0.0002474247,0.0006473046,0.0008433451,0.0001534684],"domain_scores_gemma":[0.9509498,0.03841364,0.003716777,0.003370138,0.002871488,0.00067819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001120249,0.0004548334,0.6321489,0.0003861907,0.0009851652,0.0004714862,0.0004301121,0.2795824,0.01836951,0.003528022,0.001636759,0.06088636],"study_design_scores_gemma":[0.00002926641,0.000437279,0.1015168,0.00004272788,0.00009627527,0.0002873525,0.0001145763,0.882505,0.009331658,0.004060977,0.00151048,0.00006765554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374887,0.0004955821,0.05592779,0.000309889,0.00002311125,0.00005330606,0.002032059,0.00142894,0.002240591],"genre_scores_gemma":[0.9632046,0.0001468821,0.032438,0.00009753338,0.00001622439,0.00005297177,0.003543867,0.0001693058,0.0003308186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01468193,"threshold_uncertainty_score":0.07764643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02786857942445234,"score_gpt":0.3651255642499936,"score_spread":0.3372569848255412,"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."}}