{"id":"W2116623092","doi":"10.1093/molbev/msu121","title":"Bayesian Inference of Infectious Disease Transmission from Whole-Genome Sequence Data","year":2014,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; National Institute for Health and Care Research","keywords":"Biology; Markov chain Monte Carlo; Genomics; Bayesian probability; Evolutionary biology; Inference; Computational biology; Population genomics; Bayesian inference; Population; Genome; Genetics; Computer science; Artificial intelligence","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":[0.004606692,0.0005279917,0.000903328,0.001864745,0.0005204674,0.00146031,0.001227576,0.001099049,0.0007802087],"category_scores_gemma":[0.02251907,0.0008480557,0.0007651439,0.001334729,0.001129993,0.002854872,0.0009554729,0.001781486,0.0003425827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051239,"about_ca_system_score_gemma":0.0007302382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003593678,"about_ca_topic_score_gemma":0.004739522,"domain_scores_codex":[0.9984637,0.001005896,0.0000753003,0.0002395377,0.000154201,0.00006120821],"domain_scores_gemma":[0.9916676,0.006652196,0.0008570597,0.0004156289,0.0002454442,0.0001621984],"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":[0.0002772365,0.0001443886,0.05152968,0.0003555295,0.0004879978,0.0003435008,0.0006078325,0.7943236,0.0159095,0.0666593,0.001034202,0.06832728],"study_design_scores_gemma":[0.00001832597,0.0000324454,0.007706771,0.00005201797,0.00004047333,0.0001651974,0.000113796,0.8696249,0.001707613,0.119517,0.0009776736,0.00004388975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1688917,0.0006715101,0.8283216,0.0006424087,0.00002332194,0.00002345,0.0005042317,0.0002659547,0.000655928],"genre_scores_gemma":[0.7803554,0.001019736,0.2164583,0.0002233527,0.00004471598,0.00004322616,0.001361473,0.0001179094,0.0003759551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004606692,"threshold_uncertainty_score":0.0243628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225546447815746,"score_gpt":0.2631258791246235,"score_spread":0.2508704146464661,"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."}}