Effects of Mid-winter Snow Depth on Stand Selection by Wolverines, <em>Gulo gulo luscus</em>, in the Boreal Forest
Bibliographic record
Abstract
Wolverines (Gulo gulo luscus) in a study area in the boreal upland forests of northwestern Alberta and northeastern British Columbia (approximately 57°N) were noted to be limited to upland landscapes, despite abundant food in adjacent lowland landscapes. Snow-tracking suggested that the species was selecting for the densest climax conifer stands for travel in search of food. It was hypothesized that snow depth was a limiting factor for Wolverines in the boreal forest during midwinter, and that they selected for this stand-type because of the buffering effect of this type of canopy on ground snow-depths. A series of snow-depth measurements were collected. Snow depths collected along Wolverine trails were very significantly lower than random snow depths collected under upland canopy (F = 32.84, df = 1, P << 0.010). There was a significant buffering effect on snow depth indicated for upland canopy (F = 11.1, df = 1, P < 0.010), while adjacent lowland canopy had no significant buffering effect on snow depth (F = 3.45, df = 1, P > 0.05). Wolverines were hypothesized to be limited to upland landscapes in the study area because of the buffering effect on snow-depth of the stand types found there, and not for reasons of food availability. Climax conifer stands were interpreted as being of high importance to Wolverine survival during winter. Conservation implications include the detrimental effect on Wolverine populations likely to result from current timber harvesting practices in the boreal forest.
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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 | 0.001 |
| 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".