Proposed attributes of national wildlife health programmes
Bibliographic record
Abstract
Wildlife health is important for conservation, healthy ecosystems, sustainable development and biosecurity. It presents unique challenges for national programme governance and delivery because wildlife health not only crosses jurisdictional responsibilities and authorities but also inherently spans multiple sectors of expertise. The World Organisation for Animal Health (OIE) encourages its Members to have wildlife disease monitoring and notification systems. Where national wildlife health surveillance programmes do exist, they vary in scope and size. Evidence-based guidance is lacking on the critical functions and roles needed to meet the OIE’s recommendations and other expectations of a national programme. A literature review and consultation with national wildlife health programme leaders identified five key attributes of national programmes: 1) being knowledge and science based; 2) fostering cross-nation equivalence and harmonisation; 3) developing partnerships and national coordination; 4) providing leadership and administration of national efforts; and 5) capacity development. Proposed core purposes include: 1) establishment and communication of the national wildlife health status; 2) leading national planning; 3) centralising information and expertise; 4) developing national networks leading to harmonisation and collaborations; 5) developing wildlife health workforces; and 6) centralising administration and management of national programmes. A national wildlife health programme should aim to identify, effectively communicate and manage the risk to or from a country’s wildlife populations. It should generate the appropriate knowledge required to improve the effectiveness of wildlife policies and systems, including identifying and assessing emerging priorities, thus facilitating early warning, preparedness and preventive actions.
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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".