{"id":"W2896693913","doi":"10.1038/s41598-018-33731-1","title":"Integrating standardized whole genome sequence analysis with a global Mycobacterium tuberculosis antibiotic resistance knowledgebase","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"European and Developing Countries Clinical Trials Partnership; Medical Research Council; Centers for Disease Control and Prevention; Genome Canada; Alfred P. Sloan Foundation; Natural Sciences and Engineering Research Council of Canada; Bill and Melinda Gates Foundation; World Health Organization; Ministerio de Economía y Competitividad; U.S. Department of Health and Human Services","keywords":"Mycobacterium tuberculosis; Whole genome sequencing; Tuberculosis; Antibiotic resistance; Antibiotics; Microbiology; Sequence (biology); Biology; Sequence analysis; Computational biology; Genome; Genetics; Virology; Medicine; Gene; Pathology","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":[0.003341062,0.0002991341,0.0008606106,0.0004177853,0.0005511089,0.0002620829,0.0001803768,0.0001317047,0.0003605807],"category_scores_gemma":[0.001696899,0.0002098481,0.0003094297,0.004091263,0.001504022,0.0002329958,0.0001233691,0.0002598135,0.0001227324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005152437,"about_ca_system_score_gemma":0.0007744583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001898202,"about_ca_topic_score_gemma":0.002939095,"domain_scores_codex":[0.9956498,0.0002515516,0.0008228276,0.001466055,0.0008949213,0.0009148219],"domain_scores_gemma":[0.9959306,0.00007678055,0.000347674,0.001645222,0.001407318,0.0005924693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001247525,0.0003839361,0.5803741,0.0003444998,0.002559463,0.004628009,0.0007228451,0.00002512025,0.3748822,0.0001508719,0.03178699,0.002894533],"study_design_scores_gemma":[0.002989831,0.00213026,0.4356935,0.001344396,0.005329581,0.003742622,0.001096433,0.004275185,0.03062636,0.002453524,0.5084475,0.001870758],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794475,0.0005644135,0.002612579,0.001876324,0.001473348,0.0006246204,0.0001234844,0.0001716116,0.01310609],"genre_scores_gemma":[0.9841349,0.00001378579,0.009527458,0.0001888644,0.0002484612,0.00001780771,0.0004358442,0.00002166376,0.005411187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4766605,"threshold_uncertainty_score":0.8557358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227133988940254,"score_gpt":0.3279275717286435,"score_spread":0.3052141728346181,"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."}}