{"id":"W4210476749","doi":"10.3390/microorganisms10020292","title":"Correlation between Phenotypic and In Silico Detection of Antimicrobial Resistance in Salmonella enterica in Canada Using Staramr","year":2022,"lang":"en","type":"article","venue":"Microorganisms","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"Provincial Laboratory of Public Health; University of Alberta; Queen Elizabeth II Health Sciences Centre; Ste. Anne's Hospital; BC Centre for Disease Control; Horizon Health Network; Public Health Ontario; Public Health Agency of Canada; Toronto Public Health; Saskatchewan Disease Control Laboratory; University of Manitoba","funders":"","keywords":"Salmonella enterica; Salmonella; Broth microdilution; Biology; In silico; Concordance; Antibiotic resistance; Genotype; Genetics; Typing; Drug resistance; Gene; Antimicrobial; Microbiology; Antibiotics; Minimum inhibitory concentration; Bacteria","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001296998,0.00009291247,0.0001618367,0.00007392815,0.00003557079,0.00000610276,0.00007969447,0.00004997087,0.000009176912],"category_scores_gemma":[0.00001735422,0.0001182648,0.0000125286,0.0002067623,0.00003399085,0.00000416862,0.0000990743,0.0001147746,1.349359e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00041059,"about_ca_system_score_gemma":0.0002853187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1265971,"about_ca_topic_score_gemma":0.6801414,"domain_scores_codex":[0.9991261,0.00009936412,0.0002937591,0.0002423281,0.00007261596,0.0001658579],"domain_scores_gemma":[0.9997164,0.00001148582,0.0001091902,0.0001320482,0.00001288326,0.00001799454],"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":[0.0001055732,0.00001157398,0.3073784,0.00001677417,0.000002805442,0.000004818031,0.00006893952,0.0003740127,0.6919633,5.846118e-7,0.000007900024,0.00006537323],"study_design_scores_gemma":[0.0005051262,0.00002564282,0.5222567,0.00002540437,0.000003707579,0.000003910098,0.0002003331,0.00007818812,0.4766113,0.00001421293,0.0001591085,0.000116354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990513,0.0002393275,0.0004011918,0.00001751399,0.00008966668,0.0001459803,0.0000376367,0.000001568984,0.00001581309],"genre_scores_gemma":[0.9994225,0.00001994165,0.0004343585,0.00002915966,0.00001656152,0.000001498974,0.00003849475,0.00001516869,0.00002231274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5535443,"threshold_uncertainty_score":0.8792189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006159603442279736,"score_gpt":0.199978504873911,"score_spread":0.1938189014316313,"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."}}