{"id":"W4221069327","doi":"10.1093/genetics/iyab229","title":"Efficient ancestry and mutation simulation with msprime 1.0","year":2021,"lang":"en","type":"article","venue":"Genetics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":508,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Directorate for Biological Sciences; National Institutes of Health; Canada Research Chairs; Deutsche Forschungsgemeinschaft; National Institute of General Medical Sciences; University of Edinburgh; Robertson Foundation; National Human Genome Research Institute; Villum Fonden","keywords":"Biology; Genetics; Mutation; Gene","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":[0.00003121711,0.00006224874,0.00004367567,0.00001425715,0.00004636398,0.00001909539,0.00002954755,0.00005709511,0.000009683952],"category_scores_gemma":[0.00001371567,0.00005704035,0.00001188611,0.00007181955,0.00002864055,6.0834e-7,0.0000262941,0.00002725016,0.00000262911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005100291,"about_ca_system_score_gemma":0.00006363619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.860511e-7,"about_ca_topic_score_gemma":0.000006352113,"domain_scores_codex":[0.9995261,0.0000228871,0.00007622271,0.0002089679,0.00008531156,0.00008050355],"domain_scores_gemma":[0.9996457,0.000004285163,0.00003834847,0.0001727981,0.00009743653,0.00004144605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002987143,0.00003243284,0.0030728,0.0000120577,0.00001203018,0.000003108352,0.00005485818,0.1593512,0.8280767,0.00003325213,0.0002156265,0.009106065],"study_design_scores_gemma":[0.0006989872,0.0001561761,0.03860885,0.00001664977,0.00002238361,0.00003256587,0.0002373678,0.0260517,0.9027511,0.00004469396,0.03117826,0.0002011944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726057,0.00134406,0.02536195,0.0001085746,0.00005136309,0.00005695098,0.000002845788,0.000006847403,0.0004617337],"genre_scores_gemma":[0.996131,0.0000884461,0.003158485,0.000129999,0.00008120375,0.000006803572,0.00005598152,0.000009157088,0.0003389419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1332995,"threshold_uncertainty_score":0.2326038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714416052983847,"score_gpt":0.2799590324191464,"score_spread":0.2628148718893079,"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."}}