Bibliographic record
Abstract
The future of veterinary medicine is best understood in the context of history. What began as a profession rooted in urban centers in proximity to horses, physicians, and medical schools, was transformed into a land grant-based agricultural profession with the arrival of the internal combustion engine in the early twentieth century. Most of the United States' current veterinary colleges are still located in towns or small cities in the middle section of the country, outside the largest metropolitan areas where most veterinarians practice companion-animal medicine. Throughout veterinarian history, substantial numbers of US students have been educated in foreign colleges and this continues today, creating an even greater geographic imbalance between the veterinary educational process and US population centers and major medical schools. Three themes deserve special attention as we celebrate the profession's 150th anniversary. We must first move beyond the land-grant culture and develop a more geographically balanced approach to establishing new veterinary colleges that are also in closer association with schools of medicine and public health. We must also facilitate more opportunities for women leadership in organized veterinary medicine, in practice ownership, in academia, and in the corporate structures that educate, hire, and interface with veterinarians. Finally, we need to expand our understanding of One Health to include the concept of zooeyia (the role of animals in promoting human health), as well as continue to emphasize veterinarians' special roles in the control and management of zoonotic diseases and in advancing comparative medicine in the age of the genome.
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.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".