The Need for Veterinarians in Biomedical Research
Bibliographic record
Abstract
The number of veterinarians in the United States is inadequate to meet societal needs in biomedical research and public health. Areas of greatest need include translational medical research, veterinary pathology, laboratory-animal medicine, emerging infectious diseases, public health, academic medicine, and production-animal medicine. Veterinarians have unique skill sets that enable them to serve as leaders or members of interdisciplinary research teams involved in basic science and biomedical research with applications to animal or human health. There are too few graduate veterinarians to serve broad national needs in private practice; academia; local, state, and federal government agencies; and private industry. There are no easy solutions to the problem of increasing the number of veterinarians in biomedical research. Progress will require creativity, modification of priorities, broad-based communication, support from faculty and professional organizations, effective mentoring, education in research and alternative careers as part of the veterinary professional curriculum, and recognition of the value of research experience among professional schools' admissions committees. New resources should be identified to improve communication and education, professional and graduate student programs in biomedical research, and support to junior faculty. These actions are necessary for the profession to sustain its viability as an integral part of biomedical research.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".