{"id":"W167946898","doi":"10.4315/0362-028x-73.1.18","title":"Development of Multitarget Real-Time PCR for the Rapid, Specific, and Sensitive Detection of Yersinia pestis in Milk and Ground Beef","year":2010,"lang":"en","type":"article","venue":"Journal of Food Protection","topic":"Yersinia bacterium, plague, ectoparasites research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Food Inspection Agency","funders":"Canadian Food Inspection Agency","keywords":"Yersinia pestis; Biology; Primer (cosmetics); TaqMan; Virulence; Yersinia pseudotuberculosis; Yersinia; Enterobacteriaceae; Yersinia enterocolitica; Plasmid; Microbiology; Polymerase chain reaction; Hybridization probe; Real-time polymerase chain reaction; Detection limit; Molecular probe; Bacteria; Gene; Chemistry; Chromatography; Genetics; Escherichia coli","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":[0.002833563,0.0011433,0.0009672831,0.001113861,0.0004743867,0.0008430981,0.001254252,0.0009952551,0.001817073],"category_scores_gemma":[0.001836601,0.0012458,0.0008882788,0.0003448542,0.0008582727,0.0008816238,0.0006015658,0.001690164,0.0007372037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006660193,"about_ca_system_score_gemma":0.0007552605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000528749,"about_ca_topic_score_gemma":0.001387723,"domain_scores_codex":[0.9976125,0.000452783,0.0001634467,0.0008453971,0.0007092569,0.0002167332],"domain_scores_gemma":[0.9986551,0.0006812642,0.0001527756,0.0001357708,0.0002662117,0.000108983],"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":[0.0001503389,0.00006655988,0.0003027507,0.00005851881,0.00001176566,0.00002851879,0.00004462708,0.0002404095,0.9931223,0.0001536099,0.00006217491,0.005758559],"study_design_scores_gemma":[0.00006202974,0.001170807,0.002790058,0.00003084618,0.00006842886,0.0003897596,0.00005448989,0.0105661,0.9817756,0.0001922513,0.002871172,0.00002836848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5004786,0.002535078,0.4899418,0.0005881749,0.0004037867,0.001066357,0.001480308,0.001352988,0.002152962],"genre_scores_gemma":[0.4857323,0.00124041,0.5000242,0.0005850868,0.0000969321,0.001515068,0.003330088,0.0002243375,0.007251708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002833563,"threshold_uncertainty_score":0.0149855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756748135522624,"score_gpt":0.2555335181144623,"score_spread":0.2379660367592361,"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."}}