{"id":"W4382541866","doi":"10.3138/jvme-2022-0111","title":"Resident Selection Criteria in Veterinary Medicine","year":2023,"lang":"en","type":"article","venue":"Journal of Veterinary Medical Education","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Internship; Selection (genetic algorithm); Medical education; Ranking (information retrieval); Medicine; Personnel selection; Specialty; Matching (statistics); Veterinary medicine; Family medicine; Computer science; Pathology; Management","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":[0.01834287,0.0001940603,0.0003497649,0.003301895,0.001614959,0.001344173,0.0007069474,0.0006663088,0.007725556],"category_scores_gemma":[0.05955463,0.0001572723,0.0003778978,0.002787291,0.0007430664,0.001076463,0.002175801,0.0007828138,0.001724604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001661163,"about_ca_system_score_gemma":0.004158972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002450682,"about_ca_topic_score_gemma":0.006686416,"domain_scores_codex":[0.9805163,0.01003819,0.003058582,0.0004634336,0.004876496,0.00104704],"domain_scores_gemma":[0.9519216,0.01748585,0.01072751,0.0008706599,0.01261131,0.006383187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004460795,0.0007164998,0.6600018,0.00139365,0.0000596409,0.0008587525,0.01504871,0.000887394,0.002023982,0.004537062,0.05783016,0.2561963],"study_design_scores_gemma":[0.00008359952,0.001559576,0.8361413,0.001973901,0.00002140861,0.00327069,0.01959794,0.00169416,0.0008870183,0.003548562,0.1311174,0.0001043658],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9002917,0.004653636,0.008320415,0.01365175,0.0008223851,0.002780247,0.002012283,0.0001391516,0.06732833],"genre_scores_gemma":[0.9666069,0.002159364,0.02018577,0.002168871,0.0003943313,0.001630059,0.001093258,0.00005906382,0.005702406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01834287,"threshold_uncertainty_score":0.09700751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1356846133913866,"score_gpt":0.4635236527755988,"score_spread":0.3278390393842122,"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."}}