Future Directions in the European Union for Veterinary Education as Related to Food-Producing Animals, with Special Reference to Greece
Bibliographic record
Abstract
During the past 50 years, procedures for raising food-producing animals have changed. Intensification of food production was necessary to keep prices low and to fulfill market demands for the continuously increasing worldwide population. Intensification of farming procedures produced many new problems, some of which had a considerable impact on public opinion about how animals are raised and how food of animal origin is produced and preserved. "Man made diseases" of animals such as bovine spongiform encephalopathy (BSE); contamination of foods with dioxins either through contamination of animal feeds or from the environment; and increased microbial resistance to drugs used for treatment, for prophylaxis of animals from infectious agents, and for growth promotion are some well-known hazards of intensified farming. Veterinarians working on food-producing animals are faced with an increased demand for foods of high quality and safety in developed countries, and higher quantities in the rest of the world. These qualitative and quantitative changes indicate that they must adjust to these new conditions. They will be most successful if their education is adjusted to meet the challenges that the public has created for them through new concepts of the production of food of animal origin. One such concept is the production of foods under fully certified procedures from the farm to the consumer's table. Food safety measures protecting public health will better be achieved if the education of the future veterinarian includes the principles of Hazard Analysis Critical Control Points (HACCP) starting at farm level. This article provides some market-driven ideas in this direction for European Union (EU) countries, including Greece.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".