{"id":"W4296911694","doi":"10.1101/2022.09.20.508733","title":"bayroot: Bayesian sampling of HIV-1 integration dates by root-to-tip regression","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Bayesian probability; Sampling (signal processing); Root (linguistics); Tree (set theory); Posterior probability; Statistics; Phylogenetic tree; Sample (material); Regression; Bayesian inference; Host (biology); Human immunodeficiency virus (HIV); Prior probability; Covariate; Computer science; Mathematics; Biology; Genetics; Virology; Combinatorics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008805838,0.0009210737,0.001595754,0.001297697,0.0009103067,0.001544374,0.003090563,0.00177585,0.0101833],"category_scores_gemma":[0.02968947,0.001206538,0.001305287,0.001501625,0.001319441,0.001951541,0.001662503,0.003156656,0.003033054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113057,"about_ca_system_score_gemma":0.001415126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007254465,"about_ca_topic_score_gemma":0.007879547,"domain_scores_codex":[0.9972749,0.001722073,0.00008860308,0.0003792583,0.0004202141,0.0001148759],"domain_scores_gemma":[0.9892371,0.008235103,0.0006633381,0.00094971,0.0006530707,0.0002616233],"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":[0.001055528,0.0001891502,0.01071817,0.000461009,0.0003308926,0.0002918041,0.0005037812,0.8098754,0.009546855,0.05884664,0.01163449,0.09654633],"study_design_scores_gemma":[0.0001048163,0.00003238875,0.0005609807,0.00001941198,0.00001800675,0.00004325047,0.00001839555,0.9811808,0.001550878,0.01472547,0.001721907,0.00002373049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04934208,0.0003245168,0.9395964,0.0002478379,0.00007424939,0.0001116704,0.001066661,0.00779239,0.001444276],"genre_scores_gemma":[0.3184451,0.0003226605,0.670487,0.0002890201,0.0000916315,0.0003798284,0.003192359,0.003704852,0.003087478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0101833,"threshold_uncertainty_score":0.0465703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746128291582257,"score_gpt":0.2607254095671002,"score_spread":0.2432641266512776,"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."}}