Characterization of Dall’s sheep ( <i>Ovis dalli dalli</i> ) post-lambing habitat in Kluane National Park and Reserve based on four decades of population monitoring
Bibliographic record
Abstract
Long-term monitoring data are valuable for providing an understanding of habitat consistently used by a species. We used annual aerial survey data collected since 1977 on Dall’s sheep ( Ovis dalli dalli Nelson, 1884) in Kluane National Park and Reserve, Yukon, to describe habitat use by sheep at two spatial scales during the post-lambing period. We used kernel density estimation to map sheep habitat use, followed by random forest modelling with topographic predictor variables to characterize terrain features used most by sheep. Elevation and distance to glacial ice were the two strongest predictors of habitat use, with the most frequently used areas characterized by mid to high elevations not directly adjacent to glacial ice. Relationships differed minimally between ram and nursery groups, suggesting that sexual segregation is due to behaviour and not terrain preference. Kernel density estimation was also used to stratify selection of sites for vegetation surveys to characterize habitat use at a finer scale. Areas used most frequently by sheep were comprised of relatively low growing vegetation. The results provide knowledge useful for management of this iconic species in a rapidly changing environment while demonstrating the value of long-term monitoring data.
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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 | 0.001 | 0.001 |
| 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".