{"id":"W3197557767","doi":"10.7554/elife.69032","title":"Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity","year":2021,"lang":"en","type":"article","venue":"eLife","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"AIDS Vancouver; University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley; National Institutes of Health; National Science Foundation","keywords":"Drug resistance; Drug; Human immunodeficiency virus (HIV); Resistance (ecology); HIV drug resistance; Antiretroviral therapy; Computational biology; Biology; Medicine; Bioinformatics; Virology; Genetics; Pharmacology; Viral load; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001398028,0.00009904543,0.000243209,0.00002772262,0.00007574983,0.000004981355,0.00008504026,0.00006924051,0.0001311646],"category_scores_gemma":[0.00003646409,0.00008687356,0.00006180014,0.00004841994,0.0001218261,0.00006512744,0.0001034723,0.0001145268,0.00001729634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003701682,"about_ca_system_score_gemma":0.0001057935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006239522,"about_ca_topic_score_gemma":0.007005552,"domain_scores_codex":[0.9991083,0.0001752259,0.000226159,0.0002078187,0.00008009348,0.0002024624],"domain_scores_gemma":[0.9995322,0.00008458106,0.00008130242,0.0002042938,0.00006961402,0.00002797949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001089019,0.001910277,0.8878806,0.0007342468,0.001870537,0.0003231057,0.0104159,0.0001035207,0.07730994,0.00501842,0.01220808,0.001136377],"study_design_scores_gemma":[0.006421227,0.00007128793,0.01478801,0.0002945804,0.00005338342,0.00002368994,0.001927022,0.0001358906,0.9719912,0.0005741769,0.003454804,0.0002647346],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676793,0.005231331,0.02547107,0.0005450628,0.00009473786,0.0001335506,0.0004319335,0.00001544655,0.0003975273],"genre_scores_gemma":[0.997225,0.0005093813,0.0004967588,0.00003732789,0.000007926198,0.000005660998,0.0001575319,0.000008395803,0.001552014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8946813,"threshold_uncertainty_score":0.3909262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08959866632145215,"score_gpt":0.3003992939576016,"score_spread":0.2108006276361495,"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."}}