{"id":"W3126247609","doi":"10.1016/j.vetmic.2021.109006","title":"Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring","year":2021,"lang":"en","type":"article","venue":"Veterinary Microbiology","topic":"Antimicrobial Resistance in Staphylococcus","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Shared Health; Canadian Veterinary Medical Association; University of Prince Edward Island; Agriculture Food and Rural Development; University of Saskatchewan; University of Guelph","funders":"U.S. Food and Drug Administration; National Institutes of Health","keywords":"Staphylococcus pseudintermedius; Biology; Antibiotic resistance; Resistome; Broth microdilution; Antimicrobial; Staphylococcus aureus; rpoB; Salmonella enterica; Microbiology; Genotype; Salmonella; Drug resistance; Staphylococcus; Genetics; Gene; Antibiotics; Minimum inhibitory concentration; Bacteria","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003466231,0.0005935344,0.001394319,0.0004191555,0.0001560491,0.00003787239,0.0004779907,0.0005539012,0.0001923449],"category_scores_gemma":[0.0005488656,0.0006531322,0.0003093907,0.00119736,0.0007190033,0.0001511297,0.0003688852,0.0006975262,0.00006001068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005997283,"about_ca_system_score_gemma":0.001218332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003425985,"about_ca_topic_score_gemma":0.0003791313,"domain_scores_codex":[0.995672,0.0003902905,0.001476286,0.001182966,0.000175342,0.001103063],"domain_scores_gemma":[0.9972399,0.0003152601,0.0005646249,0.001056232,0.0006064208,0.0002175969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005830036,0.0004422746,0.004808267,0.0007709294,0.0001855234,0.003842361,0.0005588377,0.000006657502,0.9734849,0.00005952049,0.0098432,0.0001674431],"study_design_scores_gemma":[0.00497885,0.0008490778,0.01381831,0.002834274,0.0001143436,0.000934158,0.0003492215,0.00000271768,0.820357,0.00008833469,0.1549434,0.0007302276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901503,0.003368597,0.00005843979,0.00056117,0.001391831,0.000842311,0.0005196185,0.0001150184,0.002992694],"genre_scores_gemma":[0.9867129,0.001549689,0.005558595,0.0003176135,0.00033804,0.00005850679,0.0002334651,0.0001077367,0.00512347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1531279,"threshold_uncertainty_score":0.999592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04130630070654402,"score_gpt":0.3063150634485939,"score_spread":0.2650087627420499,"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."}}