Effects of habitat quality and anthropogenic disturbance on grizzly bear (<i>Ursus arctos horribilis</i>) home-range fidelity
Bibliographic record
Abstract
In the Rocky Mountain eastern slopes of Alberta, Canada, grizzly bears (Ursus arctos horribilis Ord, 1815) live in a landscape heavily impacted by industrial development and human disturbance. We characterized the role of changing habitat quality and new disturbance features on patterns of grizzly bear seasonal home-range fidelity and drift by comparing consecutive-year seasonal home ranges. We relied on the geographic technique “spatial–temporal analysis of moving polygons” (STAMP) to examine changes in habitat quality and new development between zones of home-range fidelity, expansion, and contraction. Areas considered to be high-quality habitat were selected at a greater frequency than available and retained in zones of home-range fidelity, but also vacated during home-range contraction. Areas of decreasing habitat quality were equally present in zones of contraction, expansion, and stability. The proportion of new forest harvest areas and roads developed within the past year did not differ between zones of home-range change, but the proportion of new well sites was higher in contraction zones than in stability zones. Our results showed that while considerable drift occurs, changes in habitat quality and recent anthropogenic disturbances cannot account for annual variation in home ranges, suggesting other important factors influencing behaviour and movement.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".