{"id":"W3023386743","doi":"10.1534/g3.120.401052","title":"Inferring Parameters of the Distribution of Fitness Effects of New Mutations When Beneficial Mutations Are Strongly Advantageous and Rare","year":2020,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biology; Mutation; Evolutionary biology; Fitness landscape; Genetics; Selection (genetic algorithm); Divergence (linguistics); Genetic variation; Gene; Machine learning; Population; 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":[0.00229971,0.0004592621,0.000505791,0.001260704,0.0002929511,0.0007357937,0.0004478921,0.0007464671,0.0005037488],"category_scores_gemma":[0.01198097,0.000304406,0.0006120632,0.0005589752,0.0007937699,0.001117468,0.0005001046,0.0009167888,0.0001684727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004813037,"about_ca_system_score_gemma":0.0003451144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027355,"about_ca_topic_score_gemma":0.00257359,"domain_scores_codex":[0.9994169,0.0001960155,0.00004227166,0.0001951113,0.000103115,0.0000465968],"domain_scores_gemma":[0.9935795,0.004778744,0.0006985922,0.0005753125,0.0002443966,0.0001235166],"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":[0.0002433359,0.0001922332,0.3592402,0.0001132991,0.0003057322,0.0004509282,0.0004865246,0.5138634,0.07750049,0.004946122,0.0004206826,0.04223705],"study_design_scores_gemma":[0.00001989647,0.0001094049,0.1829517,0.00002476109,0.00007658351,0.0003615523,0.0002261048,0.7915514,0.0138333,0.0100484,0.000686737,0.0001101955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502551,0.00008682505,0.04874716,0.00005075377,0.000005119442,0.00001729899,0.0002128372,0.00009116228,0.0005337564],"genre_scores_gemma":[0.9912444,0.00006489297,0.008176869,0.0000171036,0.000003040233,0.00001790856,0.0003224334,0.00003054775,0.000122776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0027355,"threshold_uncertainty_score":0.01216221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00937705531558598,"score_gpt":0.233225380559755,"score_spread":0.223848325244169,"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."}}