SPIKE and D-PIKE: Innovative Experiences That Engage Students Early and Position Them to Succeed in Food-Supply Veterinary Medicine
Bibliographic record
Abstract
Recent trends in urbanization of the population, increased need for bio-security on large farms, and more food-animal or mixed-animal practitioners approaching retirement age are forcing a renewed focus on recruiting and training veterinary students with an interest in production-animal medicine. The increasing number of veterinary students coming from urban backgrounds has led to a need to expose these students to standard animal-production practices and to interest them in a career involving food animals. This article describes one such program developed at Iowa State University, in which 14 students obtained hands-on experience in all aspects of swine and dairy production across a wide sampling of herd size, housing style, bio-security levels, and production phases. The participating students, ranging from senior undergraduates to third-year veterinary students, gained valuable insight not only into daily farming practices but also the knowledge and skills necessary to provide quality veterinary care to these clients. The first year of this program has yielded positive feedback from all participants, including the veterinary practices, private producers, corporate sponsors, and students. Current applicants cite positive comments from past participants as motivating their interest in the program. This program has the potential to expand as an opportunity to educate selected students in the field of food-supply veterinary medicine and to help fill the anticipated void in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".