{"id":"W2978470405","doi":"10.1017/s0950268819000797","title":"Seasonality and zoonotic foodborne pathogens in Canada: relationships between climate and <i>Campylobacter, E</i>. <i>coli</i> and <i>Salmonella</i> in meat products","year":2019,"lang":"en","type":"article","venue":"Epidemiology and Infection","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Campylobacter; Salmonella; Veterinary medicine; Biology; Outbreak; Environmental health; Seasonality; Food contaminant; Geography; Food science; Bacteria; Medicine; Ecology; Virology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00238448,0.0002231465,0.000640656,0.00002212787,0.0001470275,0.00001175334,0.00004668104,0.0002781458,0.00001279731],"category_scores_gemma":[0.0008238137,0.0001205513,0.00002328914,0.0002454608,0.0001431575,0.0001732781,0.000104572,0.0004341821,0.000004776583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006653028,"about_ca_system_score_gemma":0.00002770672,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1064311,"about_ca_topic_score_gemma":0.476978,"domain_scores_codex":[0.9968184,0.001427961,0.0005476644,0.0006636351,0.00005237239,0.0004899361],"domain_scores_gemma":[0.9969261,0.00263194,0.0001773567,0.00007758157,0.00002975107,0.0001572857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003959772,0.00001537303,0.9925021,0.00004495466,0.000009361785,0.00000231764,0.00003528895,0.00000703024,0.002830456,0.0004345111,0.0001143079,0.003964686],"study_design_scores_gemma":[0.0003154989,0.000139533,0.9947842,0.00003992249,0.00001978089,0.00004955961,0.00006409416,0.00009934529,0.00004871364,0.002267383,0.001966978,0.0002049664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939337,0.001568126,0.000001151922,0.003594531,0.0001328996,0.0003729135,0.00003712083,0.00002181882,0.0003377244],"genre_scores_gemma":[0.9954445,0.003540196,0.00004154723,0.0007649735,0.00009760453,0.00001692164,0.00006824504,0.000002144834,0.00002382316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3705468,"threshold_uncertainty_score":0.8995192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410235119177579,"score_gpt":0.2352460732724452,"score_spread":0.1942225613546874,"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."}}