{"id":"W2021155015","doi":"10.1371/journal.pcbi.1002912","title":"The Timing and Targeting of Treatment in Influenza Pandemics Influences the Emergence of Resistance in Structured Populations","year":2013,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Santa Fe Institute; National Science Foundation","keywords":"Pandemic; Resistance (ecology); Transmission (telecommunications); Pandemic influenza; Node (physics); Influenza pandemic; Field (mathematics); Drug resistance; Biology; Coronavirus disease 2019 (COVID-19); Econometrics; Computer science; Statistics; Medicine; Disease; Mathematics; Ecology; Infectious disease (medical specialty); Genetics; Physics","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":[0.0003082117,0.00007945909,0.0002241646,0.00003620312,0.00009217034,0.000003271984,0.0001090974,0.0000466471,0.000006022644],"category_scores_gemma":[0.002991465,0.00004023497,0.0000237721,0.0001534584,0.0002477674,0.00002873545,0.00006352937,0.00006815448,3.31973e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003291529,"about_ca_system_score_gemma":0.00002685333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004161039,"about_ca_topic_score_gemma":0.00146161,"domain_scores_codex":[0.9989366,0.0002264316,0.0005305439,0.0001272573,0.00005734326,0.0001218432],"domain_scores_gemma":[0.9931294,0.006450371,0.0002474301,0.00007560298,0.00008578436,0.00001141743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000142455,0.00002714882,0.9181255,0.00002769879,0.0000289988,1.018975e-7,0.00121815,0.009774281,0.0005529793,0.06964742,0.00003704988,0.0005464555],"study_design_scores_gemma":[0.0001086297,0.00002694986,0.462391,0.00001701066,0.000004703994,1.009616e-7,0.0002489668,0.005895423,0.0000197913,0.5312251,0.00003050881,0.0000318293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975435,0.0008860862,0.0003247319,0.0008932923,0.00001961899,0.0002880062,0.0000101195,0.000006239162,0.00002842751],"genre_scores_gemma":[0.9893185,0.00007116375,0.01044865,0.00009205603,0.000009083493,0.00005192056,0.000003552574,0.000002536189,0.000002577392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4615777,"threshold_uncertainty_score":0.358128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2754462213684516,"score_gpt":0.4307393130251646,"score_spread":0.155293091656713,"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."}}