Veterinary Medical Education for Modern Food Systems: Setting a Vision and Creating a Strategic Plan for Veterinary Medical Education to Meet Its Responsibilities
Bibliographic record
Abstract
BACKGROUND Veterinary medicine had its origins in the supply of food. Nurturing the health of animals that are a source of food has been a central constituent of its professional responsibilities since its ancestral inception. The industrialization of civilizations gave veterinary medicine an expanded focus as supplying food of animal origin moved from being just part of the activities of individual small producers to larger and larger productionunitsthatareoftendistant fromlargepopulationcenters. Three major factors have arisen within the last several decades. Their dynamics now dominate the decisions of veterinary medicine as a whole and of food-systems veterinary medicine in particular: 1. Those now living within the increasingly urbanized communities have lost their connection with their formerly close neighbors who produce their food. 2. Increasing competition and technological advances have stimulated the development of today’s modern, often highly integrated, animal-food industry, with its ever-increasing demands for efficiency and cost-effective production. This has changed the entire set of dynamics between food-supply animal owner, food-supply processor and distributor, food-system veterinarian, and consumer.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".