Integrating a Bovine Rectal Palpation Simulator into an Undergraduate Veterinary Curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are problems associated with teaching bovine rectal palpation to undergraduate veterinary students. The students need opportunities to examine enough cows to develop the required skills, but increasing student numbers and limitations on access to cows have made this more and more difficult to achieve. A virtual reality-based teaching tool, the Bovine Rectal Palpation Simulator, has been developed as a supplement to existing training methods. The student palpates computer generated virtual models of the bovine reproductive tract while interacting with a haptic (touch feedback) device. During training sessions, the instructor follows the student's actions inside the virtual cow on the computer screen and gives instruction. A trial integration of the simulator into the fourth-year bovine reproduction course was undertaken at the University of Glasgow Veterinary School during the 2003/2004 academic year. Students were offered two training sessions, and feedback was gathered using questionnaires. In the first session, all students were taught a range of basic skills using a standardized teaching protocol. The second training session was customized to each student's learning needs and included practice in dealing with a range of on-farm scenarios. Student feedback indicated that the training had been useful for learning various aspects of bovine rectal palpation and provided information that helped in the further development of the simulator as a teaching tool.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it