{"id":"W2064778256","doi":"10.1016/s0168-9525(00)02141-7","title":"Detecting the form of selection from DNA sequence data","year":2000,"lang":"en","type":"article","venue":"Trends in Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biology; Fixation (population genetics); Selection (genetic algorithm); Evolutionary biology; Genetics; Human evolutionary genetics; Sequence (biology); Allele frequency; Population; Allele; Mutation rate; Background selection; DNA sequencing; DNA; Phylogenetics; Artificial intelligence; Gene; 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":[0.002566078,0.0004515389,0.0008533116,0.003501803,0.0004835666,0.001407314,0.0006895997,0.001151199,0.0009798304],"category_scores_gemma":[0.01065399,0.0003794945,0.0004799199,0.002623897,0.0008345665,0.001864176,0.0006733615,0.001534849,0.0004441665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002788885,"about_ca_system_score_gemma":0.0003822795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003123638,"about_ca_topic_score_gemma":0.0008121812,"domain_scores_codex":[0.9986824,0.0003886711,0.0001066166,0.0004014408,0.0003304002,0.00009045311],"domain_scores_gemma":[0.9919968,0.004857123,0.00133429,0.0007670927,0.0006179943,0.0004267025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001684741,0.0006592607,0.340922,0.0006565815,0.0007474678,0.001147463,0.0005421623,0.01601213,0.3802622,0.01019426,0.002576597,0.2445952],"study_design_scores_gemma":[0.0003190697,0.001656291,0.3876098,0.0001648816,0.0006344581,0.005952023,0.0009771809,0.3921924,0.1157511,0.08553242,0.008955204,0.0002551244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9409161,0.0005995742,0.05504781,0.0004738434,0.0000785437,0.00005867782,0.0007942364,0.0005149404,0.001516275],"genre_scores_gemma":[0.9750038,0.0002881727,0.02294752,0.0001840531,0.0001083299,0.00002909623,0.001035181,0.00006030546,0.00034355],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003501803,"threshold_uncertainty_score":0.0135709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0361548012497148,"score_gpt":0.309931290878402,"score_spread":0.2737764896286872,"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."}}