{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002478358,0.0001009965,0.0002263905,0.00003571523,0.0001357551,0.00001466249,0.000151533,0.00006443027,0.0001121617],"category_scores_gemma":[0.001428636,0.00004865071,0.0001358129,0.000132909,0.0001389743,0.0002657919,0.00002921707,0.00008299787,9.4136e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003411979,"about_ca_system_score_gemma":0.0001380231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002089693,"about_ca_topic_score_gemma":0.000002077382,"domain_scores_codex":[0.9989142,0.0001198158,0.0003716834,0.0001467786,0.0003172107,0.0001302536],"domain_scores_gemma":[0.9986451,0.0005483588,0.0003230881,0.00003248635,0.000195554,0.0002553692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002523493,0.002773314,0.0568761,0.0003596027,0.0001253254,0.0001680706,0.003528496,0.000001213513,0.2959666,0.000127395,0.002260059,0.6352903],"study_design_scores_gemma":[0.0002525575,0.006038009,0.988072,0.0001863947,0.00006404584,0.001230847,0.001891187,0.0000518972,0.0002103385,0.0001014198,0.00180381,0.00009744299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969161,0.001680193,0.00001015995,0.0008927874,0.0002424563,0.0002138365,0.00002664222,0.000008684734,0.000009104268],"genre_scores_gemma":[0.9986501,0.0004765649,0.0001203653,0.0001351906,0.0005279124,0.0000615831,0.00001783901,0.000001797007,0.000008699385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.931196,"threshold_uncertainty_score":0.1983918,"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."}}