Wolf, <em>Canis lupus</em>, Den Site Selection in the Rocky Mountains
Bibliographic record
Abstract
Because mortality of Wolves, Canis lupus, is highest during the first six months of life, den site selection may affect reproductive success of Wolf populations. We studied fine-scale denning habitat selection (within 100 m of den site) by comparing field-measured characteristics of 22 dens in Idaho, Montana, and Alberta with 22 paired random contrast locations within pack home ranges. In order of importance, Wolves denned in areas with greater canopy cover, hiding cover, herbaceous ground cover, and woody debris, and were closer to water than paired random sites. Thus Wolves may select den sites for physical protection and available water. We also studied coarse-scale denning habitat selection by comparing 35 Wolf dens with 35 paired contrast locations in Idaho, Montana, and Wyoming with respect to six remotely-sensed variables (elevation, slope, coniferous forest cover, solar radiation, distance to water, and distance to roads). Although these variables did not differ (univariate P > 0.10) between den and contrast locations, a Mahalanobis-distance model using four remotely-sensed variables (slope, elevation, coniferous forest cover, and solar radiation) suggested > 85% of dens would occur in potential denning habitat occupying < 12% of the Wolf recovery areas in the northern Rocky Mountains. This model may be useful for identifying likely den locations in areas not yet occupied by Wolves. Wolf core use areas, including den areas, showed higher intensity of use throughout the year when compared to the entire territory.
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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 | 0.000 | 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".