A community-of-practice-built database to support the implementation and operation of national and subnational wildlife health surveillance systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Historically, poor data management has hampered the establishment and operation of wildlife health surveillance (WHS) systems and limited the integration of environmental data into One Health frameworks. Effective WHS purpose-built databases are key to solve this problem, yet the few options available remain inaccessible or narrow in scope. To address this gap, an international partnership is developing the Health and Wildlife Knowledge (HAWK) database. HAWK supports the management of diverse data generated by multiple actors and methodologies, all within a harmonized structure and vocabulary facilitating data access, analysis, communication, and reuse. Data are secured through compartmentalization across organizations and users, while supporting compliance of FAIR and CARE data principles. Slated for release in late 2025, HAWK is envisioned as a global public good to encourage data compatibility and best practices in the wildlife conservation and One Health communities, independent of languages and location, with minimal to no cost for users.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it