{"id":"W3198214709","doi":"10.1101/2021.08.31.457499","title":"Efficient ancestry and mutation simulation with msprime 1.0","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":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; National Institutes of Health; Deutsche Forschungsgemeinschaft; University of Edinburgh; Robertson Foundation; Rannís; Villum Fonden","keywords":"Computer science; Mutation; Key (lock); Quality (philosophy); Tree (set theory); Population; Software; Sequence (biology); Data science; Data mining; Theoretical computer science; Programming language; Biology; Genetics; Computer security; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001733178,0.0002991238,0.0002203406,0.00006639415,0.00009621236,0.0001158208,0.0001352964,0.0003992604,0.000007286577],"category_scores_gemma":[0.00007643557,0.0003122477,0.0000548,0.0001439135,0.0001064146,0.000002996718,0.0002394295,0.000230308,0.000003507761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000455818,"about_ca_system_score_gemma":0.0003700422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001142332,"about_ca_topic_score_gemma":0.000006358231,"domain_scores_codex":[0.9984904,0.0000724314,0.0002392114,0.0007456142,0.0002048144,0.0002475304],"domain_scores_gemma":[0.9986613,0.00001167758,0.0001989366,0.0006146943,0.0003748839,0.0001385285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005184939,0.00009924197,0.005831832,0.0001781946,0.0001131473,0.00002289769,0.000008691514,0.309205,0.6844109,0.00005175153,0.00002180818,0.000004739334],"study_design_scores_gemma":[0.001809161,0.0003551133,0.390271,0.0004427942,0.0002586554,2.698414e-7,0.0000282415,0.3097686,0.2931944,0.000001262472,0.002084814,0.001785658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551845,0.001697193,0.04244857,0.00005052974,0.0002286924,0.0002853722,0.00004519713,0.00005120471,0.000008720634],"genre_scores_gemma":[0.9918801,0.0001360593,0.007552463,0.0001214853,0.0002014731,0.00003804501,0.000004657011,0.00005774988,0.000007974198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3912165,"threshold_uncertainty_score":0.9999329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009064506111545518,"score_gpt":0.2295760931903706,"score_spread":0.2205115870788251,"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."}}