Wildlife health perceptions and monitoring practices in globally distributed protected areas
Bibliographic record
Abstract
The status of health monitoring practices in protected areas (PAs) is largely unknown, but potential gaps could undermine biodiversity conservation at these key sites. There is also a lack of baseline information regarding local perceptions of wildlife, human, and livestock health relevance that could affect health monitoring implementation in PAs. To address these deficiencies, we conducted a web-based survey of data managers from PAs worldwide. Specifically, we assessed perceptions regarding wildlife health and pathogen transmission between wildlife, humans, and livestock; the detection and documentation of unhealthy wildlife (injured, sick, and dead) and domestic animals in PAs; and health data management. Eighty-six out of 128 responses were analyzed. Respondents considered WH relevant to the conservation goals of PAs (97%), and 98% of them confirmed that unhealthy wildlife are encountered. However, >50% and >20% of respondents claimed that injured or sick and dead animals were not recorded, respectively. When these animals were documented, the recording methods and information collected differed. Although respondents considered domestic animal presence common and a conservation concern, these animals or their health status may not be recorded (30% and 74%, respectively). Health data were often stored in a database, but paper forms and spreadsheets were also used. Responses suggested that valuable syndromic wildlife health surveillance data from PAs are not collected or are lost due to inadequate management and their value could be limited by a lack of standardized recording protocols.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".