{"id":"W2001686155","doi":"10.3138/jvme.35.2.255","title":"Training Veterinary Personnel for Effective Identification and Diagnosis of Exotic Animal Diseases","year":2008,"lang":"en","type":"article","venue":"Journal of Veterinary Medical Education","topic":"Vector-Borne Animal Diseases","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of State","keywords":"Identification (biology); Training (meteorology); Medical education; Medicine; Veterinary medicine; Disease; Pathology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007750147,0.0003095348,0.0002836277,0.0008475068,0.00147346,0.0009627247,0.0008696872,0.001344737,0.007343101],"category_scores_gemma":[0.01107678,0.0003504628,0.0002693584,0.0002540825,0.0006572865,0.0006765034,0.00180438,0.001220692,0.001802464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008084641,"about_ca_system_score_gemma":0.006085644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002119083,"about_ca_topic_score_gemma":0.00543087,"domain_scores_codex":[0.997668,0.001095861,0.0001437741,0.0001186902,0.0004019474,0.0005716557],"domain_scores_gemma":[0.9924707,0.002268892,0.0008812845,0.0002993123,0.001805784,0.0022739],"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":[0.000445234,0.003954587,0.07840148,0.002597566,0.00002932796,0.002276579,0.01634449,0.00273962,0.03848037,0.00276039,0.0437376,0.8082328],"study_design_scores_gemma":[0.000422734,0.0108075,0.5525875,0.004413652,0.0000757903,0.007571834,0.03560023,0.007038897,0.01128834,0.00513565,0.3647816,0.0002762452],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7537553,0.006201983,0.0635527,0.07922499,0.0041916,0.008668065,0.0002077175,0.001272,0.08292555],"genre_scores_gemma":[0.8575274,0.004291471,0.1048228,0.00864826,0.00110064,0.001943028,0.0001495376,0.00004713696,0.02146968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007750147,"threshold_uncertainty_score":0.04098719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1220909367854896,"score_gpt":0.3350333959446224,"score_spread":0.2129424591591328,"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."}}