Winter Habitat Selection by Muskrats in Southern Boreal Wetlands
Bibliographic record
Abstract
Muskrats (Ondatra zibethicus), as a semi-aquatic furbearer, are highly dependent on water levels and adjacent food resources to help them survive northern winters. In some areas, muskrats also act as an indicator species for monitoring changes in wetland ecosystems, such as in the deltas of the Mackenzie, Peace, and Athabasca rivers where both muskrat numbers and water levels have declined. To determine which environmental factors are most influential for winter habitat selection by muskrats, we applied a linear mixed-model approach to analyze the relationship among the number of muskrat lodges and push-ups relative to various abiotic (e.g., water depth, degree of shoreline development, pond size) and biotic factors (beaver presence, vegetation characteristics). Our study was restricted to pothole wetlands in Alberta’s southern mixed-wood boreal forest. We used a geographic information system to assess lodge location and push-up distribution relative to the most important environmental variables arising from our models. Our research provides greater insight into a species that plays an important role as both predator and prey within wetland ecosystems. *Indicates faculty mentor.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".