Landscape permeability for grizzly bear movements in Washington and southwestern British Columbia
Bibliographic record
Abstract
Providing opportunities for grizzly bears (Ursus arctos) to move between blocks of habitat is important for the long-term conservation of grizzly bear populations. While the particulars of grizzly bear habitat selection during long-distance movements are poorly understood, some landscape characteristics such as road density and land cover type are correlated with grizzly bear habitat use at various scales. We compiled digital maps of roads, human population density, land cover class, and topography to evaluate the resistance of the year 2001 landscape to grizzly bear movement in Washington State and adjacent portions of Idaho and British Columbia (BC). We developed habitat association and dispersal habitat suitability models based on published literature and used geographic information system (GIS) weighted-distance and least-cost analysis techniques to evaluate landscape permeability for grizzly bear movement. Our analysis identified 5 blocks of potential grizzly bear habitat in Washington and adjacent areas, including the Columbia–Selkirk Mountains, the North Cascades, the Central Cascades, the South Cascades, and the Coast Range. We evaluated landscape permeability between these habitat blocks and highlighted potential linkage areas. Our models indicated the Stevens Pass fracture zone between the North and Central Cascades blocks was the most permeable, followed (in order of relative permeability) by the Fraser–Coquihalla fracture zone between the North Cascades and the Coast Range, the Okanogan–Kettle fracture zone between the North Cascades and the Columbia Mountains, and the Snoqualmie Pass fracture zone between the Central and South Cascades. This evaluation provides a consistent measure of the expected potential for grizzly movement across a broad landscape that can be used to target areas for finer-scale evaluation and help identify landscape management priorities at a regional scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".