Impact of spatial and temporal resource distribution on rabies dynamics in the Arctic
Bibliographic record
Abstract
In the Arctic, rabies is endemic in the Arctic fox ( Vulpes lagopus), posing a significant and ongoing health risk for people and domestic animals. The mechanisms by which rabies is maintained within the low-density fox populations in the Arctic remain unclear. In this study, we developed a spatially explicit individual-based stochastic epidemiological model and performed an uncertainty analysis to better understand Arctic fox rabies dynamics. Rabies persisted in 25.68% of model simulations, with several variables having significant impact on rabies persistence: probability of rabies transmission, spatial and temporal distribution food resources, mean litter size and variability of rabies incubation periods. Where rabies is endemic, we identified 5 key parameters for rabies dynamics: spatiotemporal resource distribution, probability of birth for adult females, mean and standard deviation of litter size, and incubation period of rabies. Our study demonstrates that Arctic rabies can persist in its primary host under conditions consistent with existing empirical data in the literature and showed the important role played by the spatial and temporal distribution of resources. Finally, our results suggest that the ecological impacts of rapid climate warming could decrease the overall persistence of rabies in the Arctic and the associated health risk in Arctic communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".