Bibliographic record
Abstract
Veterinary specialist diplomas were available in many European countries during the second half of the 20th century. However, such an early recognition of the importance of veterinary specialization actually delayed the concept of the European veterinary specialist in Europe, compared with the United States, where the first specialist colleges were established in the 1960s, because it was felt that the national system was functioning properly and there was therefore no need for a new structure in the European countries. The European Board of Veterinary Specialisation (EBVS) was established in 1996, and currently there are 23 specialist colleges with more than 2,600 veterinarians officially listed in the EBVS register as European specialists. The Advisory Committee on Veterinary Training (ACVT) approved the establishment of EBVS but never implemented a supervising body (with ACVT representation). Such a body, the European Coordinating Committee on Veterinary Training, was later implemented by the profession itself, although it still lacked a political component. Each college depends on the EBVS, which has the function to define standards and criteria for monitoring the quality of college diplomates. To become a European Diplomate, veterinarians must have gone through an intensive period of training supervised by a diplomate, after which candidates must pass an examination. Although the term European veterinary specialist still does not have any legal recognition, national specialist qualifications are being phased out in many countries because of the inherent higher quality of EBVS specialist qualifications.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".