Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada
Bibliographic record
Abstract
The correlation of landscape features with genetic discontinuities reveals barriers to dispersal that can contribute to understanding present and future spread of wildlife diseases. This knowledge can then be used for targeting control efforts. The impact of natural barriers on raccoon dispersal was assessed through genetic analysis of samples from two regions, Niagara (N = 666) and St. Lawrence (N = 802). These areas are transected by major rivers and are at the northern front of a raccoon rabies epizootic. Genetic clusters were identified in each region using Bayesian clustering algorithms. In the Niagara region, two clusters were identified corresponding to either side of the Niagara River. For the St. Lawrence region, spatially congruent clusters were not identified, despite the presence of the intervening St. Lawrence River. These genetic data are consistent with raccoon rabies incidence data where rabies has been detected across the St. Lawrence River in Ontario while no cases have been detected in Ontario across the Niagara River. This is despite expectations of rabies incidence in Niagara before the St. Lawrence based on the progression of rabies from New York. The results from the two regions suggest different permeabilities to raccoons between New York and Ontario that may be attributed to the rivers. However, other factors have also been explored that could contribute to this difference between these study sites including the shape of the landscape and resource distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".