{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006494487,0.0000773451,0.0001458872,0.000112007,0.00006388997,0.00001192617,0.00004899998,0.0001137706,0.000002700198],"category_scores_gemma":[0.0001274809,0.00006163018,0.00003969408,0.00007514258,0.00008052156,0.00001253068,0.00002725023,0.0001741135,1.762528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839218,"about_ca_system_score_gemma":0.00006178793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000232776,"about_ca_topic_score_gemma":0.0001845405,"domain_scores_codex":[0.9993133,0.00005532154,0.0002931921,0.0001155758,0.0001182627,0.000104365],"domain_scores_gemma":[0.9993453,0.00004878171,0.000250004,0.00007950493,0.0002385982,0.00003778042],"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":[0.0009092613,0.00003672358,0.000178938,0.00003680104,0.00005581382,6.007909e-7,0.0003100076,0.000003637235,0.9683409,0.00000115808,0.000005216004,0.03012089],"study_design_scores_gemma":[0.000792048,0.001157318,0.08330341,0.00002341806,0.00001156388,0.0001169971,0.0001680793,0.0001413379,0.9133488,0.00001441256,0.0008649797,0.00005766213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962445,0.0001882169,0.002983545,0.00006079772,0.0001018712,0.0003980643,0.000007696052,0.000001116843,0.00001414007],"genre_scores_gemma":[0.9928162,0.0001823854,0.006870517,0.000002380284,0.0000989359,0.000008660394,0.000002553737,0.000008377637,0.00001000126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08312447,"threshold_uncertainty_score":0.2513205,"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."}}