Creation and Evaluation of a Food Animal Curriculum Roadmap for Veterinary Students at the University of Minnesota
Bibliographic record
Abstract
Less than 5% of US veterinary school graduates go on to practice predominantly food animal medicine, contributing to a serious shortage of veterinarians practicing in rural areas. Exposing students to clinical and farm experiences while in veterinary school is an effective way to recruit them to various types of veterinary careers. Students at the University of Minnesota College of Veterinary Medicine (UMN CVM) were not always aware of food animal course options within the curriculum. Additionally, food animal faculty had noted that while face-to-face mentoring was the most effective way to help students select courses, it was too dependent upon faculty availability and students’ comfort level in reaching out for advice. Consequently, it was decided to develop an online catalog of course options focusing on food animal topics. This course catalog, called the Food Animal Curriculum Roadmap, was designed as an interactive roadmap similar to a map of subway lines, where each line represents a species of interest (beef, dairy, small ruminant, swine, and poultry) and each station is a course. The roadmap was made available to all students at the college. A user survey showed that 96% of the respondents ( n = 30) indicated that they had a better understanding of course offerings after using the Food Animal Curriculum Roadmap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".