{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000373677,0.0002309766,0.0002626367,0.001046577,0.001537222,0.001019867,0.0005515697,0.0002268897,0.001380587],"category_scores_gemma":[0.001090222,0.000229187,0.0003895697,0.003392022,0.0005748462,0.0002211073,0.0005823935,0.0003736682,0.0001199023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01790769,"about_ca_system_score_gemma":0.02336412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959837,"about_ca_topic_score_gemma":0.9982342,"domain_scores_codex":[0.9995888,0.00003016759,0.00002350844,0.00007880565,0.0001426404,0.000136084],"domain_scores_gemma":[0.9983875,0.00009048997,0.0002528339,0.00004046101,0.000884466,0.0003443518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006212009,0.00001379369,0.9952282,0.00002345536,0.00003747462,0.00003563733,0.0004105557,0.0001860707,0.0005619849,0.00004493379,0.0003791779,0.003016619],"study_design_scores_gemma":[0.000001064964,0.000007674628,0.9988573,0.000007573961,0.000008402356,0.00001514453,0.0005244996,0.0001821183,0.00005441636,0.000007538772,0.0003311534,0.000003035419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939454,0.0004933826,0.0001619855,0.0001646126,0.00000563674,0.00001985643,0.003794225,0.00001130996,0.001403579],"genre_scores_gemma":[0.9971697,0.0003204152,0.0002840306,0.00004436884,0.000002741014,0.000009278748,0.001382415,0.000004222492,0.0007828814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01790769,"threshold_uncertainty_score":0.12993,"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."}}