Winter habitat selection by female moose in western interior montane forests
Bibliographic record
Abstract
Winter range has been identified as an important component of moose ( Alces alces (L., 1758)) conservation in managed forests, yet there have been few studies on habitat associations in montane ecosystems. We investigated habitat selection by moose at landscape and stand scales during late winter in southeastern British Columbia using global positioning system (GPS) collars on 24 adult moose cows in each of two winters. The strongest determinant of late-winter range at the landscape scale was decreasing elevation, while moose also selected for areas of gentler slopes and higher solar insolation. Elevation likely is a surrogate for snow depth, which is probably the primary causative factor influencing late-winter distribution of moose. Within late-winter range, topographic variables had little influence on moose habitat selection. Lower crown closure was the strongest determinant of stand-scale selection, although the resultant model was weak. We found no disproportionate selection for stands with high crown closure, and there was little evidence for greater use of cover stands with increasing snow as winter progressed. Within late-winter range, moose selected forage habitats (42% use vs. 30% availability) over cover habitats (22% use vs. 37% availability). The delineation of late-winter moose range can be based on snow depth, or elevation as its surrogate.
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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".