A community-of-practice-built database to support the implementation and operation of national and subnational wildlife health surveillance systems
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.
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 | 0.034 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".