Characteristics of Wolverine (<i>Gulo gulo</i>) dens in the lowland boreal forest of north-central Alberta
Bibliographic record
Abstract
We investigated Wolverine (Gulo gulo) denning ecology in the boreal forest of northern Alberta. During winters 2015/2016 and 2016/2017, we used live traps to capture four female Wolverines and fitted them with global positioning system (GPS) collars programmed to take a location every two hours. We determined reproductive status at capture and GPS location data were used to identify den sites. One female denned in one of the two years, one female denned in two consecutive years, and two females did not den during the study. Seven of the eight Wolverine den sites were in mature or old Black Spruce (Picea mariana) stands, where dens consisted of a hollow, moss-covered mound originating from a partially uplifted root mass caused by a leaning or fallen tree. One den was located under decayed logging debris with an overstorey dominated by dense deciduous regeneration. Maximum snow depth recorded (December–March) at weather stations in the study area was 32–51 cm. Spring snow coverage was scarce in our study area (<1%) and always associated with ice cover on lakes and large ponds; mean distance from dens to nearest spring snow coverage was 15.19 km (SD = 2.73, n = 8). Female Wolverines appear to be using locally-available denning structures in the lowland boreal forest, despite a lack of deep snow, persistent spring snow cover, or large boulders documented in other studies.
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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 | 0.000 |
| 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".