{"id":"W2588401929","doi":"10.1128/jcm.01945-16","title":"The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data","year":2017,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; University of Lethbridge","funders":"Division of Electrical, Communications and Cyber Systems","keywords":"Subtyping; Concordance; Epidemiology; Exposome; Computational biology; Data science; Biology; Bioinformatics; Medicine; Computer science; Genetics; Pathology; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01058102,0.0001704759,0.001649846,0.00003117551,0.000300529,0.00002683309,0.001857011,0.0003372934,0.00001730569],"category_scores_gemma":[0.08680645,0.00009749937,0.0005353122,0.00003537712,0.001465096,0.00009172387,0.0006884316,0.0009073319,0.000008397709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002173927,"about_ca_system_score_gemma":0.0003665547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006343155,"about_ca_topic_score_gemma":0.0000130896,"domain_scores_codex":[0.9957542,0.0006615497,0.002765693,0.0003684121,0.0000628762,0.00038721],"domain_scores_gemma":[0.9759731,0.01665973,0.004860446,0.00172992,0.0005797165,0.0001970649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01534075,0.0009246447,0.7291301,0.0002914345,0.001662396,0.0001431508,0.00004086348,0.00001952677,0.04484551,0.008217741,0.0744945,0.1248894],"study_design_scores_gemma":[0.007729521,0.003004993,0.5946804,0.001552533,0.0004591654,0.001198405,0.00006604104,0.0009038376,0.0008952577,0.007887905,0.3812765,0.0003454577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.933602,0.002401943,0.04588317,0.01261696,0.004211311,0.000512312,0.0006712214,0.00001679513,0.0000843384],"genre_scores_gemma":[0.9337246,0.0008199321,0.06191851,0.001463829,0.001956954,8.572651e-7,0.00007247311,0.00001710491,0.00002578462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3067819,"threshold_uncertainty_score":0.9208857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2607124774694666,"score_gpt":0.5163996312184498,"score_spread":0.2556871537489832,"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."}}