{"id":"W4200184532","doi":"10.1101/2021.12.20.473549","title":"Background selection under evolving recombination rates","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Institutes of Health; University of British Columbia","keywords":"Recombination; Selection (genetic algorithm); Biology; Background selection; Evolutionary biology; Genome; Negative selection; Recombination rate; Mutation rate; Natural selection; Population; Genetics; Gene; Computer science; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001703867,0.0003434133,0.0004642867,0.0004424559,0.000305028,0.001249353,0.0007979947,0.0006732634,0.001733193],"category_scores_gemma":[0.004402405,0.0002037055,0.0004090339,0.000583727,0.0007349205,0.0006476226,0.0007200433,0.0008432865,0.0003749779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006892285,"about_ca_system_score_gemma":0.000268216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008955666,"about_ca_topic_score_gemma":0.0005681295,"domain_scores_codex":[0.9988264,0.0003625808,0.00005615205,0.0004623355,0.0001636862,0.0001287295],"domain_scores_gemma":[0.9982539,0.0007797999,0.0003664895,0.0002816659,0.0001830702,0.0001350596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007420663,0.0001050245,0.1153841,0.0003569282,0.0006534961,0.001050871,0.0005555133,0.1101173,0.6237177,0.08560667,0.001325405,0.06038504],"study_design_scores_gemma":[0.0002265607,0.0008898798,0.2066245,0.0001653026,0.0007477933,0.002708299,0.000493374,0.5109539,0.1623885,0.09692851,0.01756546,0.0003080218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448128,0.0005987092,0.04696652,0.0002693927,0.00004624399,0.00002034993,0.0003683847,0.0003027483,0.006614917],"genre_scores_gemma":[0.9944925,0.0001580133,0.004155424,0.0001311172,0.00001765988,0.00001576793,0.0002271777,0.00006086055,0.0007414636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001733193,"threshold_uncertainty_score":0.00901103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226920721891601,"score_gpt":0.2401253408341052,"score_spread":0.2278561336151892,"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."}}