Ecological Approaches to Studying Zoonoses
Bibliographic record
Abstract
Concern over emerging infectious diseases (EIDs) and a better understanding of their causes has resulted in increasing recognition of the linkages among human, animal, and ecosystem health. It is now well recognized that human activities can promote the emergence of infectious diseases through the large-scale modification of natural environments and inadvertent vectoring (e.g., international trade and travel). These perturbations can alter the ecological and evolutionary relationships among humans, wildlife, and the pathogens that move between them, resulting in disease emergence. In recent years, the rise in zoonotic EIDs has not only increased our awareness of the need for cross-sectoral collaborations, but has also highlighted the disconnect between current ecological theory and biological reality. As the One Health movement continues to gain steam, further integration of ecological approaches into the One Health framework will be required. We discuss the importance of ecological methods and theory to the study of zoonotic diseases by (i) discussing key ecological concepts and approaches, (ii) reviewing methods of studying wildlife diseases and their potential applications for zoonoses, and (iii) identifying future directions in the One Health movement.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".