{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006395521,0.0001020333,0.0002948021,0.00007445891,0.00004258359,0.000004070973,0.00003235208,0.000197988,0.0001405924],"category_scores_gemma":[0.0009557304,0.0000968364,0.00007691863,0.0001718599,0.0001478522,0.00002843098,0.00004354392,0.0001705502,0.00001456091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029252,"about_ca_system_score_gemma":0.0002225239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001506937,"about_ca_topic_score_gemma":0.0000319872,"domain_scores_codex":[0.9984696,0.0004932546,0.0002924716,0.0003808038,0.00004672291,0.0003171538],"domain_scores_gemma":[0.9994079,0.00008166552,0.00004535839,0.0002268874,0.00005939432,0.0001788091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001076007,0.0002875862,0.8605716,0.0001208071,0.00005149663,0.0002504609,0.0000412544,0.00001446757,0.05275565,0.07951473,0.00009000061,0.006194381],"study_design_scores_gemma":[0.001322687,0.0002512862,0.9514131,0.00005134563,0.00005582454,0.0002072886,0.0001939644,0.0006929795,0.00154587,0.04248628,0.001629217,0.0001501428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708698,0.005361137,0.01696961,0.004664143,0.0001675146,0.0001731079,0.000009910474,0.00002182384,0.001762889],"genre_scores_gemma":[0.99585,0.0000825658,0.001286152,0.002487945,0.00002967459,0.0000156584,0.0001654337,0.00000673507,0.00007579723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09084155,"threshold_uncertainty_score":0.3948874,"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."}}