{"id":"W2990312921","doi":"10.12688/wellcomeopenres.15603.1","title":"Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe","year":2019,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Medical Research Council; Wellcome; Royal Society; Wellcome Trust; National Institute of Environmental Health Sciences; National Institute for Health and Care Research; Bill and Melinda Gates Foundation","keywords":"Mycobacterium tuberculosis; Whole genome sequencing; Pyrazinamide; Genetics; Tuberculosis; Biology; Computational biology; Genome; Medicine; Gene","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":[0.000976195,0.001697695,0.0008273098,0.001497541,0.0004702834,0.001415573,0.001551752,0.00083999,0.01822238],"category_scores_gemma":[0.004234474,0.001083356,0.002185295,0.001039948,0.0002373776,0.001584465,0.001413535,0.001144774,0.01508091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000464052,"about_ca_system_score_gemma":0.001062416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006135193,"about_ca_topic_score_gemma":0.01018535,"domain_scores_codex":[0.9994988,0.00007026144,0.00004564645,0.0002162453,0.0001193865,0.0000495677],"domain_scores_gemma":[0.9990578,0.0004311808,0.0001168166,0.000188055,0.0001332873,0.00007280336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003714268,0.0002881225,0.03169414,0.004963243,0.001742964,0.001746794,0.001173748,0.03640061,0.06331016,0.006597855,0.6349551,0.213413],"study_design_scores_gemma":[0.0006708308,0.0004689057,0.02746755,0.0007612006,0.0005851547,0.00193096,0.0003393754,0.4720837,0.07339416,0.01269687,0.4090438,0.0005574689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.04744875,0.001211453,0.1385121,0.0006892761,0.0003496294,0.0001858723,0.1961466,0.6077296,0.007726759],"genre_scores_gemma":[0.1817452,0.001280749,0.3508568,0.0008154497,0.0001533695,0.0005486621,0.4025093,0.05558145,0.00650905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01822238,"threshold_uncertainty_score":0.06095999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2429485752146492,"score_gpt":0.4360581042075432,"score_spread":0.1931095289928939,"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."}}