{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001935907,0.0002992195,0.000847817,0.00004695488,0.000378465,0.00002645816,0.0006138863,0.0001219149,0.00007413275],"category_scores_gemma":[0.00031591,0.0001432057,0.0001445805,0.0005591284,0.0002806773,0.00003808472,0.0005393762,0.0004314609,0.000004054723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138607,"about_ca_system_score_gemma":0.00002699165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517925,"about_ca_topic_score_gemma":0.006085571,"domain_scores_codex":[0.9960865,0.001696597,0.000764323,0.0006745529,0.0001046046,0.0006733651],"domain_scores_gemma":[0.9972321,0.001853132,0.0005579236,0.0001768371,0.00005415063,0.0001258808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004274555,0.0001074965,0.6203244,0.000005392129,0.00007855234,0.000007616463,0.0005593084,0.0005028471,0.376341,0.00003298624,0.001358556,0.0002543888],"study_design_scores_gemma":[0.0004348533,0.0002980975,0.9916034,0.00001618484,0.00003202617,0.00001479421,0.0004548398,0.00008910092,0.0005825747,0.00007431,0.006089845,0.000309943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967458,0.0002508025,0.00002314728,0.001764234,0.0001728939,0.00037735,0.0005856042,0.00002746156,0.00005273733],"genre_scores_gemma":[0.9985262,0.0001311148,0.0001192693,0.0008841896,0.00005877073,0.0000150199,0.0001787053,0.0000066836,0.00008006995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3757584,"threshold_uncertainty_score":0.5839761,"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."}}