Grizzly Bear Emigration and Land Use: An Interdisciplinary Case Study of the Greater Yellowstone Ecosystem
Bibliographic record
Abstract
The Greater Yellowstone Ecosystem (GYE) is the largest tract of wild land remaining in the lower 48 states however its habitat is fragmented by private land development, roads, mining activity and other human activities. The flagship species in the GYE is the grizzly bear (Ursus arctos horribilis) which persists here at this southernmost North American latitude. This GYE subpopulation has been isolated from other grizzly bear subpopulations in the United States for around a century. As a result, some scientists have measured a loss of genetic diversity. Retaining or reestablishing usable habitat connectivity between both the GYE and the Northern Continental Divide Ecosystem in Montana and Alberta and the Selway-Bitterroot Ecosystem in Idaho and Montana would help mitigate this genetic loss. Using Geographic Information System analysis, factors that appear to contribute to how far grizzly bears have emigrated from the GYE in northward direction include large centers of human population and one section of interstate highway. The GYE itself is reviewed: history, resources and threats. Available land use planning options (e.g., county, state, federal, wilderness, buffer zones) are addressed and the more promising conservation options for the GYE are identified. Off-road vehicles and climate change complete the list of treated topics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".