{"id":"W4383645981","doi":"10.1093/molbev/msad156","title":"Inferring Human Immunodeficiency Virus 1 Proviral Integration Dates With Bayesian Inference","year":2023,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"AIDS Vancouver; University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health; National Institute for Health and Care Research","keywords":"Biology; Bayesian probability; Inference; Human immunodeficiency virus (HIV); Bayesian inference; Latency (audio); Bayes' theorem; Computational biology; Antiretroviral therapy; Machine learning; Computer science; Artificial intelligence; Statistics; Virology; Viral load; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006353704,0.0008678549,0.001247649,0.002475931,0.0009393418,0.002009663,0.001625559,0.001678993,0.002041621],"category_scores_gemma":[0.03029811,0.001601813,0.001769697,0.001670183,0.0008331029,0.00198713,0.001370857,0.002916009,0.0007981969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326815,"about_ca_system_score_gemma":0.00269527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01876394,"about_ca_topic_score_gemma":0.02589517,"domain_scores_codex":[0.9980398,0.001162984,0.0001109705,0.0002961107,0.0002826992,0.0001074261],"domain_scores_gemma":[0.9854648,0.0122306,0.0007700477,0.0005567442,0.0007439766,0.0002339087],"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":[0.0002466562,0.0001382936,0.02096592,0.0002068617,0.0003077454,0.0001402708,0.0002210319,0.8675726,0.003206677,0.0191905,0.002114676,0.08568878],"study_design_scores_gemma":[0.00002816721,0.00002655607,0.001619243,0.00003738483,0.00003546658,0.00006355924,0.00002476552,0.9761347,0.001151738,0.01948435,0.001354684,0.0000393784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04917953,0.0006135361,0.9471562,0.0001943076,0.00003669016,0.00006361725,0.0008285895,0.001064185,0.0008633166],"genre_scores_gemma":[0.3702886,0.0007596047,0.6233474,0.0001924074,0.00009620951,0.0002243316,0.003151808,0.0003732937,0.00156639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01876394,"threshold_uncertainty_score":0.03730941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145109064976005,"score_gpt":0.2950611396214186,"score_spread":0.2836100489716585,"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."}}