{"id":"W3008091198","doi":"10.1534/genetics.119.302892","title":"The Temporal Dynamics of Background Selection in Nonequilibrium Populations","year":2020,"lang":"en","type":"article","venue":"Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"National Human Genome Research Institute","keywords":"Biology; Selection (genetic algorithm); Evolutionary biology; Dynamics (music); Genetics; Statistical physics; Physics; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008771313,0.00008774256,0.00008311529,0.00001898306,0.00005676734,0.00001414121,0.000152589,0.00009402142,0.000007171127],"category_scores_gemma":[0.00003596262,0.00007960863,0.00004916362,0.0001868812,0.00007303135,0.000001509447,0.00007699795,0.00007130172,0.000004368875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001674343,"about_ca_system_score_gemma":0.00007043591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001690579,"about_ca_topic_score_gemma":0.003077131,"domain_scores_codex":[0.9992731,0.00004572749,0.0002486311,0.0001717941,0.0001021936,0.0001585679],"domain_scores_gemma":[0.9996561,0.000006566606,0.00007638571,0.0001362497,0.00006747753,0.00005726105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000269789,0.000188895,0.5162808,0.00007480068,0.00008902452,0.00000190553,0.000276154,0.1388066,0.3207722,0.007659163,0.009299605,0.006281073],"study_design_scores_gemma":[0.001084301,0.0007370835,0.1690578,0.000009570984,0.00003211805,0.00001322505,0.0005813567,0.7804878,0.007371114,0.0008776739,0.03932983,0.0004181178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785345,0.0005195414,0.01886832,0.001314782,0.0000988801,0.0001522,0.00002305158,0.00000859044,0.0004801105],"genre_scores_gemma":[0.9952314,0.0001421879,0.004003512,0.0001245425,0.0001030606,0.00000489493,0.0001498238,0.00001489782,0.0002256692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6416812,"threshold_uncertainty_score":0.3246346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02430713406479532,"score_gpt":0.2740791973969733,"score_spread":0.249772063332178,"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."}}