Temporal evolution of the ecological niche of the white- footed mouse (Peromyscus leucopus) and its relation with the emergence of Lyme disease in Québec
Bibliographic record
Abstract
It is expected that climate change will influence the distribution of a number of species. As some of these species are disease vectors, changes are also expected to occur in the distribution of disease risk areas, which influence the disease transmission risk to human populations. In this thesis I describe the potential influence of climate change on the white-footed mouse distribution (Peromscus leucopus), a range that is expanding within Québec. The white-footed mouse is an important zoonotic reservoir for the Lyme disease pathogen (Borrelia burgdorferi) and is a vector of the disease's principal host: the black-legged tick (Ixodes scapularis). My results provide information to guide intervention activities and to target sites for disease surveillance. I first characterize, based on published literature, the environmental, geographical, and climatic determinants of the current range of the mouse. I then use Ecological Niche Factor Analysis to explore the influence of climate on the distribution of the white-footed mouse in Québec. Finally, I model the mouse's current fundamental niche at its northeastern range limit, using a combination of habitat-niche models from BIOMOD, and estimate its future expansion along three scenarios of projected climate change. I conclude by,predicting a potential expansion of the range of the white-footed mouse into most of the province of Québec by 2050, as a result of warmer and shorter winters.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".