{"id":"W2080381308","doi":"10.1016/j.prevetmed.2011.05.004","title":"Veterinary syndromic surveillance: Current initiatives and potential for development","year":2011,"lang":"en","type":"review","venue":"Preventive Veterinary Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":153,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Veterinary public health; Disease surveillance; Public health surveillance; Animal health; Public health; Population; One Health; Data science; Grey literature; Medicine; Risk analysis (engineering); Computer science; Environmental health; Veterinary medicine; MEDLINE; Pathology; Political science","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":[0.006097403,0.001214823,0.001993338,0.00392707,0.0003067855,0.002213417,0.001504585,0.001786585,0.003443122],"category_scores_gemma":[0.00815584,0.000438305,0.0009940123,0.003690747,0.0009726311,0.002571047,0.001411862,0.002363885,0.0008966582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00213879,"about_ca_system_score_gemma":0.008368585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286738,"about_ca_topic_score_gemma":0.009447479,"domain_scores_codex":[0.9986708,0.0003323861,0.0002649836,0.0002069319,0.0004332729,0.00009169002],"domain_scores_gemma":[0.9877675,0.007528126,0.001496212,0.000158093,0.002526359,0.0005237879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004787395,0.00005815093,0.000800325,0.0228608,0.00009856615,0.00008542556,0.00007118851,0.0003592151,0.0003739236,0.002817406,0.01329049,0.9591365],"study_design_scores_gemma":[0.00004383754,0.0002391464,0.00469275,0.05688914,0.0006634084,0.001121088,0.0004973843,0.0007231611,0.000663703,0.003402464,0.9310034,0.0000604092],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001235269,0.9973722,0.0003935893,0.001109363,0.000270305,0.000009937275,0.00003410348,0.00001467611,0.0006723304],"genre_scores_gemma":[0.001400667,0.9963906,0.001081754,0.0005917244,0.0002434177,0.00001038909,0.00005354341,0.000002156452,0.0002257566],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006097403,"threshold_uncertainty_score":0.03224653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1437555848130295,"score_gpt":0.4120846947216823,"score_spread":0.2683291099086528,"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."}}