{"id":"W4323542811","doi":"10.14745/ccdr.v49i23a04","title":"Quality over quantity in active tick surveillance: Sentinel surveillance outperforms risk-based surveillance for tracking tick-borne disease emergence in southern Canada","year":2023,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Institut National de Santé Publique du Québec; Centre Intégré de Santé et de Services Sociaux des Laurentides; Public Health Agency of Canada; Santé Montérégie; University of Saskatchewan; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Sherbrooke","funders":"","keywords":"Disease surveillance; Enzootic; Tick; Epidemiological surveillance; Geography; Environmental health; Medicine; Veterinary medicine; Disease; Epidemiology; Virology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002211969,0.000606907,0.00109377,0.0002217521,0.000550342,0.00004789849,0.0008646672,0.000177634,0.0002830483],"category_scores_gemma":[0.005879292,0.0006374456,0.0002687262,0.001153846,0.0002681447,0.0001703586,0.0002341984,0.0007347839,0.00001618817],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364808,"about_ca_system_score_gemma":0.01211108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9870363,"about_ca_topic_score_gemma":0.9992705,"domain_scores_codex":[0.9942369,0.001453293,0.001480441,0.001047171,0.0003880853,0.001394051],"domain_scores_gemma":[0.9940125,0.00188046,0.0008691725,0.002366773,0.0004531811,0.0004179141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001079526,0.0003015331,0.9849314,0.0002645048,0.0001724969,0.000797108,0.00005815496,0.005722089,0.0002296761,0.00003275361,0.006142015,0.0002687268],"study_design_scores_gemma":[0.002153629,0.00001679742,0.9895104,0.0000903578,0.0000316124,0.000007342061,0.0004790076,0.001570297,0.00007000758,0.00005895477,0.005216293,0.0007952902],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98817,0.002407515,0.0000274133,0.0007456272,0.000971787,0.001063006,0.006293516,0.0001833815,0.0001377299],"genre_scores_gemma":[0.9948811,0.0001992824,0.00000489786,0.0002725341,0.00004131717,0.000344693,0.003697606,0.00009101395,0.0004675476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01223423,"threshold_uncertainty_score":0.9996077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387461498302654,"score_gpt":0.2859014428376114,"score_spread":0.2620268278545848,"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."}}