Practical application of disease risk analysis for reintroducing gray wolves ( <i>Canis lupus</i> ) to Isle Royale National Park, <scp>USA</scp>
Bibliographic record
Abstract
Abstract Evaluation of disease risks associated with wildlife translocations is important for minimizing unintended harm and achieving conservation goals. A framework for disease risk analysis (DRA) has been developed by the World Organization for Animal Health and International Union for Conservation of Nature, but applications for planning and implementation in wildlife conservation projects are limited. To fill this gap, we describe a DRA we conducted to identify, assess, and mitigate disease risks associated with reintroduction of gray wolves ( Canis lupus ) to Isle Royale National Park (IRNP). A total of 19 wolves were translocated from multiple locations within the Great Lakes Region to IRNP between September 2018 and September 2019. Integration of the DRA into project planning and use of diverse expertise among project personnel enabled a timely and cost‐effective process that facilitated multidisciplinary and cross‐cultural collaboration, transparent communication about risks and uncertainties, and practical management of disease risks for wildlife and personnel. Engaging disease experts and experienced field biologists in the assessment also helped to identify and account for potential sources of bias. We hope practical examples like this encourage wider adoption of DRA principles in translocations of wildlife for conservation purposes.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".