{"id":"W3165822238","doi":"10.7554/elife.65645","title":"Understanding the evolution of multiple drug resistance in structured populations","year":2021,"lang":"en","type":"article","venue":"eLife","topic":"Drug Transport and Resistance Mechanisms","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Metapopulation; Disequilibrium; Linkage disequilibrium; Population; Evolutionary dynamics; Biology; Population genetics; Resistance (ecology); Evolutionary biology; Computational biology; Genetics; Sociology; Ecology; Medicine; Demography","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":[0.0001360374,0.00006020164,0.0001401353,0.00005022307,0.00006678711,0.000004305254,0.00004252685,0.00003643957,0.00004056694],"category_scores_gemma":[0.00006714236,0.00004323246,0.0000545927,0.0003255937,0.00003771789,0.00003079734,0.000007236744,0.0001075057,0.000001426018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067675,"about_ca_system_score_gemma":0.0001287528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003983691,"about_ca_topic_score_gemma":0.01173386,"domain_scores_codex":[0.9992939,0.00002795735,0.0002038525,0.0001207368,0.0002380395,0.0001155016],"domain_scores_gemma":[0.9996255,0.00003198288,0.00005067114,0.0002004995,0.00006006407,0.00003126952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005071134,0.0002315864,0.5089388,0.0003680319,0.00007948103,0.0001524335,0.006102939,0.0006266747,0.08689263,0.3933673,0.002635884,0.00009709251],"study_design_scores_gemma":[0.002737688,0.00001308806,0.9241598,0.0005502299,0.00009497577,0.000008351112,0.01148166,0.0006405519,0.02702585,0.03089388,0.002212691,0.000181215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427851,0.002822381,0.04504968,0.003584844,0.0004936465,0.0003200405,0.00001982766,0.00004098525,0.00488349],"genre_scores_gemma":[0.9947637,0.00002146105,0.003118523,0.0000642485,0.00004215253,0.000003988853,0.00002419965,0.000008514949,0.001953266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4152209,"threshold_uncertainty_score":0.6547771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05644135507231502,"score_gpt":0.2730597570073755,"score_spread":0.2166184019350605,"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."}}