{"id":"W4387701111","doi":"10.1093/evlett/qrad048","title":"The evolution of genetic covariance and modularity as a result of multigenerational environmental fluctuation","year":2023,"lang":"en","type":"article","venue":"Evolution Letters","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariance; Selection (genetic algorithm); Pleiotropy; Trait; Population; Biology; Evolutionary biology; CMA-ES; Quantitative genetics; Disruptive selection; Modularity (biology); Genetic drift; Covariance function; Mathematics; Natural selection; Genetic variation; Statistics; Genetics; Computer science; Artificial intelligence; Phenotype","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":[0.0007885792,0.0001745586,0.0002924393,0.0004948539,0.0003731969,0.0006775871,0.0003006556,0.0003884762,0.0008126671],"category_scores_gemma":[0.004236207,0.0001595481,0.0003964495,0.0003379392,0.0006934825,0.0005302924,0.000638759,0.0003882476,0.00006153536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006723003,"about_ca_system_score_gemma":0.0003170635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002297389,"about_ca_topic_score_gemma":0.002088351,"domain_scores_codex":[0.9997656,0.0001092574,0.00001059976,0.00004183934,0.0000355946,0.00003706396],"domain_scores_gemma":[0.9987956,0.0005818529,0.0002374542,0.0001634145,0.00009410003,0.0001276226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003210387,0.0002147305,0.1667937,0.00006821386,0.0005549113,0.0007972762,0.0006890818,0.7133525,0.07836179,0.02202628,0.0007513489,0.01606916],"study_design_scores_gemma":[0.00003938947,0.000145573,0.133718,0.000009148386,0.00007522521,0.0002075223,0.0001848301,0.8478651,0.003307207,0.01402184,0.0003774157,0.00004862957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996551,0.00001255591,0.003012963,0.0000480918,0.000002517097,0.000002967724,0.00002020609,0.00001360477,0.0003360427],"genre_scores_gemma":[0.9993255,0.000009261072,0.0005553864,0.000008198504,9.271433e-7,0.000003876246,0.00001380272,0.000003701994,0.00007934876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002297389,"threshold_uncertainty_score":0.004877865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003949484521439401,"score_gpt":0.2106627842894915,"score_spread":0.2067132997680521,"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."}}