{"id":"W2003807777","doi":"10.1016/j.prevetmed.2014.01.007","title":"GEOVET 2013: Geospatial analysis in veterinary epidemiology and preventive medicine","year":2014,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Geospatial analysis; Epidemiology; Veterinary medicine; Spatial epidemiology; Preventive healthcare; Medicine; Environmental health; Data science; Geography; Computer science; Public health; Cartography; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009755746,0.001157365,0.001607738,0.006744964,0.0009513675,0.005010453,0.002072706,0.001123958,0.03067059],"category_scores_gemma":[0.03528874,0.001302473,0.002616186,0.009671984,0.0009603464,0.003665028,0.006845201,0.001934231,0.00528698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001904087,"about_ca_system_score_gemma":0.007417123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06880286,"about_ca_topic_score_gemma":0.06279112,"domain_scores_codex":[0.994476,0.002892364,0.000555274,0.0005023199,0.001218269,0.00035574],"domain_scores_gemma":[0.9859617,0.008424252,0.0008815983,0.002083872,0.001784683,0.00086381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001499186,0.0004421061,0.07495516,0.002674923,0.002576408,0.0007982088,0.001800125,0.03933423,0.004227031,0.07241794,0.5092844,0.2899902],"study_design_scores_gemma":[0.0006722965,0.0002571194,0.128132,0.001747469,0.0009439985,0.0007086599,0.002853381,0.1494725,0.004456183,0.1120079,0.5983629,0.0003856744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06738702,0.005230884,0.4453583,0.01139547,0.002024497,0.001252511,0.3512659,0.08407739,0.03200806],"genre_scores_gemma":[0.2511389,0.002962907,0.4969743,0.001006997,0.0006202544,0.001984307,0.2174958,0.01559007,0.01222645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06880286,"threshold_uncertainty_score":0.1368048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05900622332616418,"score_gpt":0.3839658219173154,"score_spread":0.3249595985911513,"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."}}