{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004991151,0.0004882783,0.002124779,0.001045366,0.0001159499,0.000005286751,0.0002227227,0.000222035,0.002464797],"category_scores_gemma":[0.002989534,0.000368084,0.000220512,0.001088756,0.0008018555,0.0001784133,0.0002514139,0.0004872532,0.00002513979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001324898,"about_ca_system_score_gemma":0.0001053054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00245758,"about_ca_topic_score_gemma":0.00007320899,"domain_scores_codex":[0.9943946,0.001973797,0.001348737,0.001008001,0.000411056,0.0008637965],"domain_scores_gemma":[0.9960358,0.001788504,0.0004347464,0.0007312701,0.000161593,0.0008480785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01063459,0.002967741,0.4809037,0.00493411,0.006234401,0.002161251,0.01049512,0.0001178682,0.003884409,0.008529432,0.02572448,0.4434129],"study_design_scores_gemma":[0.00685689,0.01742742,0.9012884,0.001247787,0.001873278,0.0004266224,0.0008199819,0.006198085,0.000002955142,0.003883013,0.05956417,0.0004113117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568777,0.004011237,0.006634933,0.02419191,0.000546029,0.001115923,0.00003269174,0.00009238482,0.006497182],"genre_scores_gemma":[0.9912899,0.001464787,0.001035461,0.003572055,0.0008993244,0.0001127924,0.0003654423,0.00004227548,0.001218033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4430016,"threshold_uncertainty_score":0.9998771,"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."}}