Applying the resist–accept–direct (RAD) framework to wildlife health management
Bibliographic record
Abstract
Abstract Wildlife diseases can have substantial impacts on wildlife populations as well as on human and domestic animal health and well-being. Although many agencies and stakeholders share a goal of supporting wildlife health, reducing wildlife disease burden is complicated by a scarcity of effective interventions for wildlife, competition for funds, and conflicting priorities. As a result, agencies are unlikely to avoid the impacts of wildlife diseases in all contexts and need to evaluate where resisting disease is most feasible and beneficial. The resist–accept–direct (RAD) framework is a tool that assists natural resource managers in exploring and communicating about management interventions, including in situations where resisting ecological changes may not be possible. In the present article, we discuss how the RAD framework could be adapted to wildlife disease contexts to address several outstanding challenges in wildlife health management.
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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 | 0.050 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".