{"id":"W4294845406","doi":"10.1101/2022.09.06.506725","title":"Bayesian inference of admixture graphs on Native American and Arctic populations","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Markov chain Monte Carlo; Graph; Algorithm; Dense graph; Population; Computer science; Bayesian probability; Mathematics; Pathwidth; Combinatorics; Theoretical computer science; Line graph; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004148582,0.000339892,0.000559316,0.002378144,0.000949935,0.001166501,0.001101781,0.0006502359,0.001294382],"category_scores_gemma":[0.01330712,0.0005089781,0.0008963469,0.001417702,0.0009962503,0.0008533692,0.0007558452,0.001080432,0.0001317869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127054,"about_ca_system_score_gemma":0.0009023632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03053785,"about_ca_topic_score_gemma":0.03430742,"domain_scores_codex":[0.9987133,0.0007304139,0.00004054336,0.0003394766,0.0001092398,0.00006692725],"domain_scores_gemma":[0.9941677,0.004541086,0.000375773,0.0003401478,0.0004142985,0.0001609734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002078837,0.0001117921,0.1263515,0.00009244694,0.0005144135,0.0003674662,0.0009664119,0.7374916,0.006313064,0.06394663,0.001531635,0.0621052],"study_design_scores_gemma":[0.00001664857,0.00001075978,0.01618075,0.00001549496,0.00003888832,0.00003938922,0.0001291947,0.9454231,0.0008115421,0.03680137,0.0005113626,0.00002146636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6824894,0.0001565374,0.3145574,0.000262293,0.00001425217,0.00003700346,0.0004434667,0.0004217954,0.001617888],"genre_scores_gemma":[0.9462323,0.00005447264,0.0527157,0.00005115787,0.00001171129,0.00002278567,0.0005159228,0.00004974888,0.0003463005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03053785,"threshold_uncertainty_score":0.06072021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668428473054251,"score_gpt":0.2785606265214607,"score_spread":0.2618763417909182,"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."}}