{"id":"W4378781867","doi":"10.1371/journal.pcbi.1011173","title":"Contact-number-driven virus evolution: A multi-level modeling framework for the evolution of acute or persistent RNA virus infection","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"ACT-X; Core Research for Evolutional Science and Technology; National Center for Emerging and Zoonotic Infectious Diseases; Japan Society for the Promotion of Science; Secom Science and Technology Foundation; Japan Prize Foundation; JST-Mirai Program; Life Science Foundation of Japan; Moonshot Research and Development Program; Daiwa Securities Health Foundation; Shinnihon Foundation of Advanced Medical Treatment Research; Japan Agency for Medical Research and Development; Suzuken Memorial Foundation","keywords":"Virus; Viral evolution; Biology; Virology; Viral load; Population; RNA virus; Transmission (telecommunications); RNA; Host (biology); Genetics; Medicine; Gene","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":[0.0001615586,0.0001659645,0.0001899977,0.00008031711,0.0002345217,0.00001125168,0.0001738884,0.0002733192,0.00002219149],"category_scores_gemma":[0.0001984329,0.0001309867,0.0002184104,0.0002403896,0.0001124117,0.000005641512,0.0001145197,0.0001190441,0.00003303237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130301,"about_ca_system_score_gemma":0.0002275728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000160915,"about_ca_topic_score_gemma":0.0001890747,"domain_scores_codex":[0.9988332,0.00009184089,0.0003401581,0.0003512931,0.0001265623,0.0002569949],"domain_scores_gemma":[0.9990329,0.0001702756,0.000158969,0.0002015935,0.0003813594,0.00005492688],"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":[0.0005372252,0.0002115723,0.003883608,0.00004041657,0.001003625,7.053412e-7,0.0001161075,0.8378525,0.1334967,0.021908,0.0003646989,0.0005848656],"study_design_scores_gemma":[0.0007816777,0.000404851,0.007931445,0.00001961321,0.0001380387,0.00001462479,0.00009420389,0.9834877,0.000580814,0.006143929,0.0002274683,0.0001756217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2998369,0.0001526891,0.698724,0.00020773,0.0002631858,0.0003372537,0.000421203,0.00003363235,0.00002340587],"genre_scores_gemma":[0.9773234,0.0002246588,0.02091172,0.000126558,0.000211532,0.0001177055,0.0009019684,0.00002110142,0.0001613229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6778123,"threshold_uncertainty_score":0.5341481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05906392482671145,"score_gpt":0.3259354496278321,"score_spread":0.2668715248011206,"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."}}