Spatial overlap of Dall sheep, Grizzly bears and wolves in the Richardson mountains, Canada
Bibliographic record
Abstract
Dall sheep (Ovis dalli dalli) in the northern Richardson Mouncains, Canada, are at the northeastern limit of the species range, and have been declining since the mid 1990s. Factors that may have contributed to the decline include: severe climate, density-dependence, interspecific competition, diseases, harvest, and predation. In this study. we investigare the indirect effects of predation by grizzly bears (Unus arctos) and wolves (Canis lupus) on this Dall sheep population through the study of their spatial dynamics, in particular the overlap of the species composite home ranges. Between 2006 and 2009, we tracked individuals of the three species with GPS telemetry to describe their home ranges, movements and habitat use. Species composite home ranges revealed substantial overlap between areas used by a11 three species. The home range of wolves overlapped most of the Dall sheep range. When focusing on the core areas used by Dall sheep, however, ,he overlap with grizzly bears was larger. This suggests that wolves are likely encountered more often over a wider area whereas grizzly bear encounters are more likely in core areas. To refine our assessment of grizzly bear and wolf predation risk on tris Dall sheep populatioll, further analyses on individual and seasonal borne range overlap, comparative habitat use, dynamics interactions, Dall sheep vigilance behaviour, predator diet and documentation of traditional ecological knowledge are required.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".