{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002006026,0.001694986,0.0007573694,0.001869583,0.0008994126,0.003363667,0.001449672,0.004492563,0.01895518],"category_scores_gemma":[0.005220834,0.0007782911,0.0009837783,0.00150605,0.001566022,0.005719775,0.002760657,0.005797286,0.01240253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130216,"about_ca_system_score_gemma":0.001103821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008983496,"about_ca_topic_score_gemma":0.001668645,"domain_scores_codex":[0.9992488,0.0002988182,0.00009275055,0.0001065028,0.0001951963,0.00005777177],"domain_scores_gemma":[0.9964252,0.002442427,0.0001832243,0.000131424,0.0005331401,0.0002846018],"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":[0.0001455305,0.0004501501,0.001567357,0.002409743,0.00004170509,0.0007060162,0.001397371,0.004001191,0.004105024,0.08506254,0.3663926,0.5337207],"study_design_scores_gemma":[0.00001178033,0.000163999,0.0009980982,0.002076634,0.000006208838,0.001111996,0.000343147,0.002916856,0.0004085974,0.03644959,0.9554574,0.00005562566],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005818338,0.1836329,0.6075066,0.07449211,0.03144809,0.001090572,0.002133415,0.004892441,0.08898544],"genre_scores_gemma":[0.02496643,0.2312508,0.5558106,0.03937158,0.03168135,0.001847188,0.001797012,0.001663031,0.111612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01895518,"threshold_uncertainty_score":0.06341136,"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."}}