{"id":"W4386134132","doi":"10.3390/vetsci10090537","title":"Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators","year":2023,"lang":"en","type":"article","venue":"Veterinary Sciences","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"School of Veterinary Medicine, Ross University","keywords":"Python (programming language); Veterinary education; Decision tree; Random forest; Computer science; Field (mathematics); Veterinary medicine; Medical education; Artificial intelligence; Data science; Mathematics; Medicine; Curriculum; Psychology; Pedagogy","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":[],"consensus_categories":[],"category_scores_codex":[0.002416311,0.0002035568,0.0003193397,0.00084822,0.0005171362,0.00003487641,0.000263482,0.0001492104,0.0004680556],"category_scores_gemma":[0.001414385,0.0001790686,0.00005895083,0.001668407,0.0005243522,0.0005056419,0.00007836799,0.0003031843,0.00003285572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816875,"about_ca_system_score_gemma":0.001637366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001282354,"about_ca_topic_score_gemma":0.00003062186,"domain_scores_codex":[0.9974534,0.0002354368,0.0006035712,0.0006553771,0.0005411438,0.0005111046],"domain_scores_gemma":[0.9988545,0.0002506789,0.0001327245,0.0002752999,0.0001426675,0.0003441803],"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":[0.002331122,0.001937615,0.1227999,0.001719881,0.00004943999,0.0003500954,0.01848044,0.0009883606,0.1274839,0.0020602,0.007543821,0.7142553],"study_design_scores_gemma":[0.0009788154,0.08569154,0.06013273,0.00249143,0.0001322931,0.01076724,0.07166435,0.3039741,0.002304054,0.006181664,0.4539956,0.001686214],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714,0.0004860547,0.00008477113,0.02308919,0.004077481,0.000510778,0.000002010234,0.0001701548,0.0001796074],"genre_scores_gemma":[0.9928533,0.0002370632,0.00231586,0.0007180544,0.003198009,0.0001279972,0.000126761,0.00002651516,0.0003964691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7125691,"threshold_uncertainty_score":0.7302205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4428782450824044,"score_gpt":0.5363731122746428,"score_spread":0.09349486719223843,"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."}}