The Need for Veterinarians in Biomedical Research
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
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.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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