{"id":"W4385265783","doi":"10.20944/preprints202307.1831.v1","title":"Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Python (programming language); Veterinary education; Veterinary medicine; Computer science; Field (mathematics); Medical education; Data science; Artificial intelligence; Mathematics education; Medicine; Mathematics; Psychology; Curriculum; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002178047,0.001584247,0.0007278784,0.001961479,0.0008659245,0.00352037,0.001330982,0.004207629,0.02032453],"category_scores_gemma":[0.005445691,0.0007596579,0.001004693,0.001636598,0.00168368,0.005342387,0.002707959,0.005617233,0.0133453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153288,"about_ca_system_score_gemma":0.001105751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008781917,"about_ca_topic_score_gemma":0.001501585,"domain_scores_codex":[0.9991677,0.0003287809,0.00009213026,0.0001237818,0.0002229783,0.00006469297],"domain_scores_gemma":[0.9963181,0.002499721,0.0001784776,0.0001568575,0.0005520073,0.0002947494],"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":[0.0001330881,0.0003745434,0.001584943,0.002029075,0.00004176779,0.0005913541,0.001164379,0.004173071,0.003922745,0.09813052,0.3673385,0.5205161],"study_design_scores_gemma":[0.00001126398,0.0001296046,0.001142598,0.001684737,0.000006146977,0.001017946,0.0003380844,0.003585815,0.0005331117,0.04256558,0.9489268,0.00005820011],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005355919,0.1466251,0.6634657,0.07188701,0.02847183,0.000706931,0.002120754,0.005587815,0.07577894],"genre_scores_gemma":[0.03018573,0.2021987,0.567655,0.0342726,0.03519215,0.001416675,0.002089397,0.002157456,0.1248323],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02032453,"threshold_uncertainty_score":0.06799233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6152653807316354,"score_gpt":0.5583494433703621,"score_spread":0.05691593736127332,"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."}}