{"id":"W4306871314","doi":"10.1093/nar/gkac920","title":"CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2141,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Simon Fraser University; University of Manitoba; Public Health Agency of Canada; McMaster University","funders":"Michael G. DeGroote Institute for Infectious Disease Research, McMaster University; Cisco Systems Canada; Simon Fraser University; Institute of Infection and Immunity; Canadian Institutes of Health Research; Genome Canada; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; McMaster University; Cisco Systems","keywords":"Resistome; Biology; Database; Data curation; Genetics; Computational biology; Antibiotic resistance; Gene nomenclature; Genome; Gene; Antibiotics; Computer science; Mobile genetic elements; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008533847,0.002818591,0.003179289,0.007470367,0.001882347,0.005375785,0.007102906,0.00172209,0.05603278],"category_scores_gemma":[0.02048723,0.001966399,0.002551246,0.007949421,0.0007036434,0.003550852,0.006047938,0.002858238,0.0357941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002131233,"about_ca_system_score_gemma":0.009382644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0208226,"about_ca_topic_score_gemma":0.03591595,"domain_scores_codex":[0.9942946,0.0009596216,0.0008616418,0.001397054,0.001927578,0.0005596715],"domain_scores_gemma":[0.992007,0.002097636,0.0005572043,0.002285533,0.002398387,0.0006542532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006353298,0.0001039155,0.002497548,0.002580121,0.0003178963,0.0002651286,0.000266659,0.001333666,0.006852557,0.004778629,0.9275571,0.05281141],"study_design_scores_gemma":[0.000606779,0.00006554287,0.00608317,0.0004427506,0.0002228833,0.0002573744,0.0001019192,0.01583463,0.01136245,0.006580364,0.958271,0.0001712103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.004909662,0.0007682454,0.07506633,0.0006988876,0.0003200807,0.001129274,0.7860287,0.1178359,0.01324293],"genre_scores_gemma":[0.006714375,0.0003315538,0.1318717,0.0006231931,0.00006835705,0.001261048,0.8442002,0.01125902,0.003670589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05603278,"threshold_uncertainty_score":0.1874483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331706186225609,"score_gpt":0.3320995253678785,"score_spread":0.2989289067453176,"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."}}