Evolutionary perspectives on wildlife disease: concepts and applications
Bibliographic record
Abstract
Wildlife disease has the potential to cause significant ecological, socioeconomic, and health impacts. As a result, all tools available need to be employed when host-pathogen dynamics merit conservation or management interventions. Evolutionary principles, such as evolutionary history, phenotypic and genetic variation, and selection, have the potential to unravel many of the complex ecological realities of infectious disease in the wild. Despite this, their application to wildlife disease ecology and management remains in its infancy. In this article, we outline the impetus behind applying evolutionary principles to disease ecology and management issues in the wild. We then introduce articles from this special issue on Evolutionary Perspectives on Wildlife Disease: Concepts and Applications, outlining how each is exemplar of a practical wildlife disease challenge that can be enlightened by applied evolution. Ultimately, we aim to bring new insights to wildlife disease ecology and its management using tools and techniques commonly employed in evolutionary ecology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".