{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00389409,0.0003001934,0.0006654508,0.001161737,0.0004339654,0.001249074,0.0009833688,0.0008938565,0.00142379],"category_scores_gemma":[0.01776563,0.0005997379,0.0007655468,0.001174908,0.0008909047,0.001256354,0.0013519,0.0009432319,0.0002695524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007803597,"about_ca_system_score_gemma":0.0006106904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090398,"about_ca_topic_score_gemma":0.005940292,"domain_scores_codex":[0.9982821,0.0009336446,0.00008350038,0.0004696738,0.0001182075,0.0001128895],"domain_scores_gemma":[0.9928361,0.00511648,0.000894271,0.0007097167,0.0002573411,0.0001861308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005440274,0.0001428212,0.3359753,0.0003224245,0.0005553094,0.001295203,0.00202283,0.5136892,0.01564187,0.03959012,0.003555693,0.08666524],"study_design_scores_gemma":[0.00005039296,0.00008099431,0.0527393,0.000077205,0.00009045457,0.0005582851,0.0003336978,0.8662813,0.002712557,0.07360461,0.003418928,0.00005226893],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6754305,0.000423676,0.3197378,0.0004744355,0.00002930126,0.0000651677,0.001601025,0.001067574,0.001170614],"genre_scores_gemma":[0.8957365,0.0002068153,0.1011785,0.0001238524,0.00002370868,0.00007265136,0.002063334,0.0001920882,0.0004026071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004090398,"threshold_uncertainty_score":0.02059418,"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."}}