{"id":"W2577533595","doi":"10.1093/molbev/msw275","title":"Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks","year":2016,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":271,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; University of British Columbia","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Medical Research Council; National Institute for Health and Care Research","keywords":"Outbreak; Phylogenetic tree; Biology; Phylogenetics; Inference; Transmission (telecommunications); Markov chain Monte Carlo; Tree (set theory); Infectious disease (medical specialty); Evolutionary biology; Computational biology; Genetics; Computer science; Disease; Bayesian probability; Virology; Artificial intelligence; Mathematics; 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":[0.0003095915,0.00014599,0.0001897774,0.0000590932,0.00007737587,0.000003570138,0.00005641463,0.0001630442,0.000002451071],"category_scores_gemma":[0.0003262448,0.0001128414,0.00004202276,0.00003498395,0.0002360829,0.000001056002,0.0001532094,0.00004745965,0.000002945777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008211,"about_ca_system_score_gemma":0.00003804504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007544809,"about_ca_topic_score_gemma":0.0002629495,"domain_scores_codex":[0.9988509,0.0002069244,0.0002151381,0.0004206679,0.00001829091,0.0002881134],"domain_scores_gemma":[0.9995896,0.0000516702,0.00006314115,0.0001738422,0.00002529923,0.00009639739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005634211,0.00001034942,0.5059004,0.000004153027,0.00002318393,0.000001565963,0.000008175144,0.00001340944,0.4883857,0.003215282,0.000009761505,0.00237166],"study_design_scores_gemma":[0.0008463334,0.0002167584,0.9727449,0.00001179547,0.0000213028,0.00001628307,0.000008135004,0.00004517767,0.002924695,0.0210313,0.001939601,0.0001937456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97326,0.007778198,0.01799866,0.0006237926,0.00009904592,0.0001415128,0.00001984883,0.000004967514,0.00007393081],"genre_scores_gemma":[0.9979539,0.001238392,0.0003326686,0.000312693,0.00007412909,0.00003279519,0.00001774768,0.00001103246,0.00002663335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.485461,"threshold_uncertainty_score":0.4601539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018586204187254,"score_gpt":0.2596897783384694,"score_spread":0.2495039162965969,"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."}}