Bibliographic record
Abstract
Public-health issues regarding zoological collections and free-ranging wildlife have historically been linked to the risk of transmission of zoonotic diseases and accidents relating to bites or injection of venom or toxins by venomous animals. It is only recently that major consideration has been given worldwide to the role of the veterinary profession in contributing to investigating zoonotic diseases in free-ranging wildlife and integrating the concept of public health into the management activities of game preserves and wildlife parks. At the veterinary undergraduate level, courses in basic epidemiology, which should include outbreak investigation and disease surveillance, but also in population medicine, in infectious and parasitic diseases (especially new and emerging or re-emerging zoonoses), and in ecology should be part of the core curriculum. Foreign diseases, especially dealing with zoonotic diseases that are major threats because of possible agro-terrorism or spread of zoonoses, need to be taught in veterinary college curricula. Furthermore, knowledge of the principles of ecology and ecosystems should be acquired either during pre-veterinary studies or, at least, at the beginning of the veterinary curriculum. At the post-graduate level, master's degrees in preventive veterinary medicine, ecology and environmental health, or public health with an emphasis on infectious diseases should be offered to veterinarians seeking job opportunities in public health and wildlife management.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.008 |
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".