Winter habitat selection and use of clearcuts by elk in the White River drainage of southeastern British Columbia
Bibliographic record
Abstract
This study of elk winter habitat selection was conducted from June 1975 to May 1977. Surveys were conducted from November to April to observe elk use of habitat, patterns of use within clearcuts, and elk reaction to human activities and vehicle traffic. Programmes of habitat mapping, vegetation description and pellet group counts were conducted during the rest of the study period. The two winters of the study were mild. Snow depths never exceeded 45 cm, the depth hypothesized to initiate elk movement to areas of lower snowdepth. During these mild snow conditions elk selected clearcuts for feeding but utilized forested habitats for resting and escape cover. Subsequent studies in the same area (McLellan 1978) showed contrasting avoidance of clearcuts for two months during deep snow conditions where snowdepths exceeded 50 cm. Within clearcuts elk were observed to select moderate slopes further than 200 m from active roads for feeding and resting. Feeding activities within clearcuts showed selection for ridges, grass/forb vegetation and burned areas. Elk showed varying responses to slash accumulations during feeding activity. Elk selected the largest clearcut site and no preference for areas near edge of clearcuts was shown. Elk showed a strong avoidance reaction to human activity and vehicle traffic, fleeing to forest cover when disturbed. Recommendations for forest management are included.
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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 | 0.001 | 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".