Mapping the Teaching of Laboratory Animal Science and Medicine in the European Union and European Free Trade Area
Bibliographic record
Abstract
Developing a common market and allowing free movement of goods, services, and people is one of the main objectives of the European Union (EU) and the European Free Trade Area. In the field of scientific research, Directive 2010/63/EU on the protection of animals used for scientific purposes aims to improve the welfare of laboratory animals by following the principle of the 3Rs (replacement, reduction, and refinement). Each breeder, supplier, and user must appoint a designated veterinarian to advise on the well-being and treatment of the animals. In our report we investigate how the undergraduate veterinary curriculum prepares future veterinarians for the role of designated veterinarian, by analyzing data from 77 European veterinary education establishments. Over 80% of them provide training in laboratory animal science and medicine in their curriculum. All countries in the EU and the European Free Trade Area, having national veterinary schools, include such training in the curriculum of at least one of their establishments. Laboratory animal science and medicine courses can be obligatory or elective and are often part of more than one subject in the veterinary curricula. Post-graduate courses or programs are available at more than 50% of those veterinary schools. Most authorities in the European region consider graduate veterinarians ready to seek the role as designated veterinarian immediately after graduation.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".