YUKON MOOSE: I. SEASONAL RESOURCE SELECTION BY MALES AND FEMALES IN A MULTI-PREDATOR BOREAL ECOSYSTEM
Bibliographic record
Abstract
Moose (Alces alces) in Yukon experience an extreme range of thermal conditions, highly variable snow depths, natural and anthropogenic disturbances, predation by wolves and grizzly bears, and hunting pressure. Our objective was to identify variables that best explained habitat-selection patterns of moose in south-central Yukon for use in land-use planning and impact assessment. We evaluated selection of land-cover class, elevation, aspect, predation risk, and harvest vulnerability using resource selection functions. We created pooled models for males and females by averaging models for individuals by sex and season. Selection of shrub-dominated land cover highlighted the importance of forage accessibility throughout the year. Selection for elevation, aspect, and cover changed throughout the year, as influenced by climatic conditions. By selecting mixed cover types during calving and summer, female moose presumably balanced needs for both cover and forage. Males minimized harvest vulnerability during rut. Moose, in general, demonstrated highly variable habitat selection; however, consistent individual responses between sexes supported trends identified by pooled selection coefficients, as well as detected trends among males and females. The greatest amount of individual variation occurred during the growing season and the least amount during late winter, suggesting that climatic factors limited the options available to moose at a critical time of the year.
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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".