{"id":"W4309830272","doi":"10.1099/mgen.0.000891","title":"Combining analytical epidemiology and genomic surveillance to identify risk factors associated with the spread of antimicrobial resistance in Salmonella enterica subsp. enterica serovar Heidelberg","year":2022,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Ontario; University of Guelph; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency; Western University; Public Health Agency of Canada","funders":"Ontario Veterinary College, University of Guelph; Genome Canada","keywords":"Salmonella enterica; Antibiotic resistance; Biology; Transmission (telecommunications); Salmonella; Antimicrobial; Serotype; Multiple drug resistance; Livestock; Microbiology; Plasmid; Biotechnology; Drug resistance; Antibiotics; Bacteria; Gene; Genetics; Ecology","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":[0.0008304655,0.00034428,0.0002480434,0.001535127,0.0001788451,0.0007923043,0.0002130941,0.0003630254,0.0005587627],"category_scores_gemma":[0.00150885,0.0001906824,0.0003182607,0.001137543,0.0002091569,0.0003253664,0.0006021211,0.0002397349,0.0001351914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004164219,"about_ca_system_score_gemma":0.000501624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009170689,"about_ca_topic_score_gemma":0.00972005,"domain_scores_codex":[0.9992478,0.0002337952,0.00005109618,0.0002030618,0.0001241785,0.0001401256],"domain_scores_gemma":[0.9992513,0.0001802722,0.0002957698,0.00005698494,0.0001363535,0.00007922122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002267002,0.0001335952,0.9587968,0.00004937637,0.0001391244,0.0001187676,0.0002991362,0.0005865438,0.02244712,0.0002167418,0.0001676471,0.01681844],"study_design_scores_gemma":[0.000006963819,0.0002694871,0.9923052,0.00002345815,0.00009552337,0.0003043286,0.0007300249,0.002525144,0.002879164,0.0001830921,0.0006678461,0.000009713647],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947425,0.000248804,0.003141696,0.00008672425,0.000005901507,0.00003074957,0.0004257411,0.00002523899,0.001292558],"genre_scores_gemma":[0.9963834,0.0001737097,0.002767832,0.00004598987,0.000005723458,0.000009992604,0.0003317539,0.000004049485,0.000277483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009170689,"threshold_uncertainty_score":0.01823461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03345769763402606,"score_gpt":0.2552986173320643,"score_spread":0.2218409196980382,"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."}}