{"id":"W2887433979","doi":"10.11575/prism/32779","title":"Improved Wildlife Health and Disease Surveillance through the Combined Use of Local Knowledge and Scientific Knowledge","year":2018,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; ArcticNet; Canadian Wildlife Health Cooperative; Government of Nunavut; Polar Knowledge Canada; University of Calgary","keywords":"Wildlife; Sociology of scientific knowledge; Wildlife disease; Disease surveillance; Geography; Disease; Environmental planning; Knowledge management; Data science; Medicine; Computer science; Biology; Sociology; Social science; Ecology; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003934472,0.0002165203,0.0006285119,0.0001473005,0.0003748037,0.00002811513,0.0001700133,0.0001512998,0.00006222057],"category_scores_gemma":[0.0001857163,0.0001920876,0.0001247735,0.0002646217,0.000927526,0.0001662769,0.0001050861,0.0002325185,0.000003997168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006969723,"about_ca_system_score_gemma":0.003016893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654387,"about_ca_topic_score_gemma":0.0004474119,"domain_scores_codex":[0.9985818,0.000169307,0.00027757,0.0004518084,0.0002317493,0.0002877957],"domain_scores_gemma":[0.9978017,0.0001734051,0.000370147,0.0004767568,0.0003942435,0.0007837478],"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":[0.007647334,0.002174777,0.0116565,0.01857415,0.0005885831,0.00002992304,0.04462024,9.129646e-8,0.0000349388,0.009267619,0.1391224,0.7662834],"study_design_scores_gemma":[0.007231112,0.002007646,0.4687391,0.002261766,0.0006706608,0.000006902439,0.00747973,0.03490575,0.00000757772,0.0009893393,0.4749933,0.00070705],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9553321,0.02190003,0.01155592,0.003804279,0.001022403,0.002102373,0.00005294581,0.00007135606,0.004158572],"genre_scores_gemma":[0.9331163,0.004095461,0.004961574,0.0006174979,0.0001132318,0.000003540398,0.002670371,0.00008476542,0.05433726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7655764,"threshold_uncertainty_score":0.7833103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999827323781564,"score_gpt":0.2680646713659556,"score_spread":0.24806639812814,"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."}}