Practical application of disease risk analysis for reintroducing gray wolves ( <i>Canis lupus</i> ) to Isle Royale National Park, <scp>USA</scp>
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
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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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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