{"id":"W2905780132","doi":"10.1016/j.mimet.2018.12.021","title":"Targeting discriminatory SNPs in Salmonella enterica serovar Heidelberg genomes using RNase H2-dependent PCR","year":2018,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; University of Guelph; BC Centre for Disease Control; Provincial Laboratory of Public Health; University of Alberta; Ste. Anne's Hospital; Canadian Food Inspection Agency; Public Health Agency of Canada","funders":"Canadian Food Inspection Agency","keywords":"Biology; In silico; Amplicon; Genotyping; Salmonella enterica; Salmonella; Molecular Inversion Probe; Genetics; SNP genotyping; Serotype; Primer (cosmetics); Single-nucleotide polymorphism; In silico PCR; Genome; Computational biology; Polymerase chain reaction; Multiplex polymerase chain reaction; Gene; Virology; Genotype; Bacteria","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":[0.004322112,0.0003068386,0.000832091,0.00006095538,0.0001658395,0.00004126499,0.0006260519,0.0003345641,0.0005587213],"category_scores_gemma":[0.0007475027,0.0001146659,0.0003181953,0.000341811,0.0002994231,0.0001365031,0.0002640369,0.0005300071,0.00001955097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348453,"about_ca_system_score_gemma":0.00002339972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007034366,"about_ca_topic_score_gemma":0.00002181606,"domain_scores_codex":[0.9951392,0.002445011,0.001229117,0.0004254798,0.0001274176,0.0006337173],"domain_scores_gemma":[0.9975276,0.001179445,0.0007814153,0.00009178684,0.0002113816,0.0002083723],"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":[0.0001849053,0.0001560675,0.02716612,0.000005074662,0.00003363614,0.00006354514,0.00009934242,0.00002036388,0.9342115,0.00002380616,0.000260727,0.03777491],"study_design_scores_gemma":[0.003560778,0.01124078,0.4799844,0.000607839,0.0004036619,0.005488285,0.006794291,0.001557781,0.3043666,0.01738844,0.1652322,0.003374973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994009,0.002705934,0.001544236,0.000472699,0.000949055,0.0001345892,0.000013956,0.00002129305,0.0001492138],"genre_scores_gemma":[0.8980386,0.0006062477,0.09896686,0.0009369109,0.001384713,0.000002382519,0.000008560864,0.000003601095,0.0000521558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.629845,"threshold_uncertainty_score":0.6117606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07438740872312219,"score_gpt":0.351595479610813,"score_spread":0.2772080708876908,"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."}}