Veterinarians in Biomedical Research: A Perilous Future?
Bibliographic record
Abstract
INTRODUCTION Converging events in the biomedical research arena suggest that research activities relying heavily on animal use may have expanded so much that the supply of veterinarians trained in specialties related to biomedical research is inadequate. This concern led the Institute for Laboratory Animal Research of the National Academies (ILAR) to convene a committee to study a highly relevant issue facing the biomedical research community both in academia and in industry: How can more veterinarians be prepared for careers in biomedical research? The seriousness of the issue is reflected in the diverse institutions supporting the study, including the National Institutes of Health (NIH), the American College of Laboratory Animal Medicine (ACLAM), the American Society for Pharmacology and Experimental Therapeutics, the American Veterinary Medical Association (AVME), GlaxoSmithKline, Merck and Co., and Pfizer, Inc. The ILAR study was authored by a committee of experts, chaired by Dr. James Fox, of the Massachusetts Institute of Technology. This article is based on the executive summary of the committee’s report, National Need and Priorities for Veterinarians in Biomedical Research.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".