Wildlife health perceptions and monitoring practices in globally distributed protected areas
Bibliographic record
Abstract
Diseases are a threat to biodiversity conservation and global health, however, wildlife health (WH) surveillance systems remain uncommon. This deficit is especially relevant in protected areas (PAs) facing anthropogenic pressures. Integration of field conservation actors patrolling PAs can drastically strengthen WH surveillance. Nevertheless, baseline information regarding current WH monitoring mandates and practices at these sites is missing. To address this gap, we surveyed globally distributed protected area data managers (PADMs). PADMs considered WH as relevant to the conservation goals of PAs and >90% of them confirmed that non-healthy and dead wildlife are encountered. However, >50% and >20% of PADMs claimed that these animals were not recorded, respectively. When these animals were documented, the recording methods and information collected differed. Although domestic animal presence was common and considered a conservation concern, these animals and their health status were not always recorded. Health data were often stored in a database, but paper forms and spreadsheets were also used. Responses suggest that valuable syndromic WH surveillance data from PAs are being lost due to non-collection or inadequate management and their value could be limited by unstandardized documentation. Rangers could become a globally distributed “One Health workforce” but these flaws must be addressed first.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".