{"id":"W2950285143","doi":"10.1534/genetics.117.200493","title":"Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation","year":2017,"lang":"en","type":"article","venue":"Genetics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":289,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Coalescent theory; Inference; Allele frequency; Heavy traffic approximation; Population; Population genetics; Biology; Robustness (evolution); Statistical physics; Joint probability distribution; Econometrics; Allele; Evolutionary biology; Computer science; Mathematics; Genetics; Statistics; Artificial intelligence; Demography; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000144036,0.00008864353,0.00007465748,0.00002463609,0.0003732483,0.0000264795,0.0003465362,0.00009511739,0.00001548027],"category_scores_gemma":[0.0001009555,0.00005910666,0.00008081178,0.00002330611,0.0002120663,0.000003461754,0.000215384,0.00007059131,0.000002608445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001188162,"about_ca_system_score_gemma":0.00004181104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003576683,"about_ca_topic_score_gemma":0.0001830922,"domain_scores_codex":[0.9993951,0.0000472852,0.0001655941,0.0001410041,0.0001458226,0.0001051712],"domain_scores_gemma":[0.9989307,0.000007569342,0.000230714,0.0007210231,0.00008328546,0.00002669882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000403999,0.00005410927,0.6766137,0.00004303508,0.00007703438,4.961963e-7,0.00135696,0.005306841,0.2908466,0.001521455,0.01266917,0.01147016],"study_design_scores_gemma":[0.0004307678,0.00004694622,0.9115241,0.00000859724,0.00005275005,0.000005114375,0.000182057,0.002161288,0.01245556,0.00126678,0.07172429,0.000141758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937088,0.001078427,0.003211419,0.0004298375,0.0004643596,0.0001995869,0.00001432793,0.000004735256,0.0008884628],"genre_scores_gemma":[0.9973882,0.0001251948,0.001598165,0.0001509854,0.0001200904,0.000005282065,0.00006285244,0.000007904567,0.0005413435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2783911,"threshold_uncertainty_score":0.2870762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04102147723750198,"score_gpt":0.2423694308195897,"score_spread":0.2013479535820877,"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."}}