Genetic structure of the white‐footed mouse in the context of the emergence of Lyme disease in southern Québec
Bibliographic record
Abstract
The white-footed mouse (Peromyscus leucopus) has expanded its northern limit into southern Québec over the last few decades. P. leucopus is a great disperser and colonizer and is of particular interest because it is considered a primary reservoir for the spirochete bacterium that causes Lyme disease. There is no current information on the gene flow between mouse populations on the mountains and forest fragments found scattered throughout the Montérégie region in southern Québec, and whether various landscape barriers have an effect on their dispersal. We conducted a population genetics analysis on eleven P. leucopus populations using eleven microsatellite markers and showed that isolation by distance was weak, yet barriers were effective. The agricultural matrix had the least effect on gene flow, whereas highways and main rivers were effective barriers. The abundance of ticks collected from mice varied within the study area. Both ticks and mice were screened for the presence of the spirochete bacterium Borrelia burgdorferi, and we predicted areas of greater risk for Lyme disease. Merging our results with ongoing Lyme disease surveillance programs will help determine the future threat of this disease in Québec, and will contribute toward disease prevention and management strategies throughout fragmented landscapes in southern Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".