{"id":"W3161806733","doi":"10.1093/molbev/msab149","title":"Fundamental Identifiability Limits in Molecular Epidemiology","year":2021,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; AIDS Vancouver; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; University of Toronto; Canadian Institutes of Health Research; Genome Canada; Deutsches Krebsforschungszentrum; National Science Foundation","keywords":"Biology; Phylogenetic tree; Identifiability; Inference; Bayesian probability; Bayes' theorem; Prior probability; Context (archaeology); Data set; Statistics; Evolutionary biology; Computational biology; Genetics; Computer science; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04993942,0.001130605,0.002206242,0.00375076,0.001806512,0.004498837,0.002914927,0.003921767,0.003188176],"category_scores_gemma":[0.2295045,0.001260728,0.00207892,0.002250456,0.01314653,0.01362021,0.005789494,0.009259532,0.0004998344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002743442,"about_ca_system_score_gemma":0.001741303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001021194,"about_ca_topic_score_gemma":0.0004155637,"domain_scores_codex":[0.9772752,0.01605014,0.0008442681,0.002613779,0.00258729,0.0006292962],"domain_scores_gemma":[0.6139067,0.3588887,0.01037283,0.01100328,0.004464592,0.001363821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003060256,0.00002383948,0.001854427,0.0002176765,0.00006488938,0.0001260751,0.000355919,0.02315523,0.0004458686,0.9608999,0.001130683,0.01169482],"study_design_scores_gemma":[0.000009586517,0.00001287088,0.0001993049,0.00005851277,0.000006724546,0.0000481894,0.00003277984,0.04860524,0.0001359498,0.9501477,0.0007251858,0.00001795123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0227419,0.003309077,0.9555374,0.01162663,0.0002161834,0.00008985007,0.0003360381,0.0001621958,0.005980715],"genre_scores_gemma":[0.699663,0.01052912,0.2770804,0.00383832,0.002917835,0.001320744,0.000965212,0.0002253498,0.003460025],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04993942,"threshold_uncertainty_score":0.264108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281539770981846,"score_gpt":0.3553570832436398,"score_spread":0.3325416855338214,"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."}}