Bibliographic record
Abstract
The veterinary profession has gone through periods of profound change in response to economic and social changes. We are currently in another such period: profound change is required in order for the profession to remain relevant in a marketplace where a rapidly expanding knowledge base and new technologies demand an ever-increasing level of expertise in a greater variety of areas. However, the veterinary profession is perceived both internally and by the public as possessing a narrow set of skills that supports a narrow group of careers focused on salvaging ill or injured companion animals. It will be necessary to dramatically change the way veterinary students are recruited and trained, as well as how graduate veterinarians are licensed and provided continuing education, in order for the veterinary profession to capitalize on our historical strengths and provide service and leadership in a greater diversity of career paths. Even though the number of veterinarians needed to provide primary care for livestock is decreasing, both the level of expertise demanded by livestock owners and the value of veterinary involvement on livestock farms are increasing. Colleges of veterinary medicine appear challenged to meet the changing needs for veterinary services in animal agriculture because of the declining percentage of veterinary students interested in food animal careers. Fortunately for animal agriculture, the skill set needed by food animal veterinarians is also needed by several emerging segments of the veterinary profession that have tremendous potential for rapid growth, including employment in all segments of food production systems, environmental monitoring and management, bio-security and disease eradication, laboratory diagnostics, and federal regulatory and bio-defense roles. Like previous periods of profound change, this moment in history will require creative thought, open discussion, and a willingness to step into the unknown.
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 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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".