The ECVP/ESVP Summer School in Veterinary Pathology: High-Standard, Structured Training for Young Veterinary Pathologists
Bibliographic record
Abstract
This article describes the ECVP/ESVP Summer School in Veterinary Pathology, a new annual two-week European training facility established by the European College of Veterinary Pathologists (ECVP) in collaboration with the European Society of Veterinary Pathology (ESVP). The aim of the Summer Schools is to provide Europe-wide, harmonized, top-standard theoretical and practical post-graduate training for veterinarians specializing in veterinary pathology. In particular, it aims to support trainees in veterinary pathology in their individual preparation for the ECVP certifying examination. Ultimately, it aims to provide young pathologists with the skills and knowledge necessary to participate in international, high-quality research and the tools for applying international standards to their own research and for independent study for the ECVP certifying examination, even if they do not work in comparable academic environments and do not have the same level of local support and training. The ECVP/ESVP Summer Schools take place in European countries, with local organization from a university department of veterinary pathology. Each event comprises modules provided by internationally recognized specialists in their specific fields of expertise on different organ systems, diseases of specific species, specific techniques, and specific topics relevant to pathology, forming a cycle of four events to cover all major topics. Every two years a mock exam is organized as a tool to monitor individual progress in preparing for the ECVP certifying examination.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".