{"id":"W3138666549","doi":"10.1177/10406387211003910","title":"The Ontario Animal Health Network: enhancing disease surveillance and information sharing through integrative data sharing and management","year":2021,"lang":"en","type":"article","venue":"Journal of Veterinary Diagnostic Investigation","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ministry of Agriculture, Food and Rural Affairs","funders":"","keywords":"Preparedness; Context (archaeology); Disease surveillance; Variety (cybernetics); Disease; Government (linguistics); Data sharing; One Health; Information sharing; Medicine; Data science; Environmental health; Computer science; Public health; Pathology; Geography; Alternative medicine; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009188275,0.0001033095,0.0001613546,0.00004573592,0.0005324793,0.0001742449,0.0001765006,0.00003745193,0.00002457951],"category_scores_gemma":[0.0006094447,0.00007801745,0.0000259805,0.0001171825,0.0001358031,0.001093607,0.0005569725,0.0002582444,0.000004634967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172791,"about_ca_system_score_gemma":0.0002319036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006164837,"about_ca_topic_score_gemma":0.001486888,"domain_scores_codex":[0.9989507,0.0001637311,0.000437709,0.0001743592,0.00005712999,0.0002163379],"domain_scores_gemma":[0.9988688,0.0003896812,0.0002748117,0.0002494218,0.000145591,0.00007164329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003059178,0.0004940538,0.7696965,0.002549336,0.002350112,0.0008584093,0.02038096,0.001209939,0.03502708,0.06700025,0.03292612,0.06444801],"study_design_scores_gemma":[0.002131937,0.001160008,0.9170095,0.002136702,0.0001324238,0.001568031,0.001948173,0.0005813369,0.0007836424,0.005359683,0.06680974,0.0003788011],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881066,0.008266772,0.001021999,0.001960038,0.0003380894,0.0001961093,0.00002098842,0.00000876151,0.000080654],"genre_scores_gemma":[0.9910217,0.007711952,0.0005818203,0.0003594337,0.00006194594,0.000007418912,0.0001823903,0.000005994388,0.00006737019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.147313,"threshold_uncertainty_score":0.4095454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05530946357717863,"score_gpt":0.318844994029017,"score_spread":0.2635355304518384,"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."}}