{"id":"W3175871955","doi":"10.1099/mgen.0.000607","title":"SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data","year":2021,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Mycobacterium research and diagnosis","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"European and Developing Countries Clinical Trials Partnership; Medical Research Council; Canadian Statistical Sciences Institute; European Commission; Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation; Foreign, Commonwealth and Development Office; Genome Canada","keywords":"Mycobacterium tuberculosis; Tuberculosis; Whole genome sequencing; Computational biology; Data science; Realm; Biology; Public health; Mycobacterium tuberculosis complex; Genome; Microbiology; Computer science; Medicine; Genetics; Geography; Gene","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":[0.0002201842,0.0002206911,0.000388876,0.0001118055,0.0001419846,0.0002866562,0.0002069684,0.0001423047,0.0007300692],"category_scores_gemma":[0.000317026,0.000235466,0.00008587692,0.0002976426,0.00005757644,0.0001929556,0.0008249962,0.0002523273,0.0004773568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390455,"about_ca_system_score_gemma":0.0003958481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006734644,"about_ca_topic_score_gemma":0.0005791018,"domain_scores_codex":[0.9982343,0.00008139332,0.0003517629,0.0007277146,0.0001477917,0.0004569837],"domain_scores_gemma":[0.9981874,0.0001130516,0.0000470535,0.001061477,0.0001682327,0.0004227407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001283313,0.0002045759,0.01348567,0.00007263868,0.0002052596,0.0001361856,0.0002573276,0.000001930606,0.9494479,0.00001317641,0.03227493,0.003772023],"study_design_scores_gemma":[0.002378967,0.0002204376,0.487464,0.0001600995,0.0004222049,0.0003237487,0.0002269449,0.00008592078,0.1470558,0.00006819627,0.3610426,0.0005510881],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919682,0.0004360095,0.0002141307,0.002405501,0.0004876572,0.0004188409,0.003714104,0.00005753277,0.0002980441],"genre_scores_gemma":[0.9799437,0.002004589,0.00752905,0.002793828,0.00102669,0.00003910187,0.005811176,0.00006968083,0.0007821822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8023921,"threshold_uncertainty_score":0.9602026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03564570103421923,"score_gpt":0.3244036412700992,"score_spread":0.28875794023588,"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."}}