Potential conflict between future development of natural resources and high-value wildlife habitats in boreal landscapes
Bibliographic record
Abstract
We used the Muskwa-Kechika Management Area in northeast British Columbia, Canada as a case study to determine potential conflicts between future resource development and high-value habitats of large mammals in an undeveloped boreal landscape. More than 50 % of high-value habitats for caribou, moose, elk, wolves and grizzly bears were located in Special Resource Management Zones, where natural resource developments could occur. We developed geographic information system (GIS) layers of potential forest resources, oil and gas, minerals, wind power, all resources combined, and roads; and quantified the proportions of high-value habitats overlapping these potentials. Greater proportions of high-value habitats across seasons for moose, elk, and wolves overlapped areas with high cumulative resource potential (winter, 49–70 %, growing season, 35–63 %) more than for three other species (grizzly bears, Stone’s sheep, mountain goats). This pattern was similar for forest resources, oil and gas, wind power, and roads. Caribou were more seasonally influenced. The proportions of their high-value habitat in areas with high cumulative resource potential (winter, 53 %, growing season, 16 %), as well as high forest and oil and gas potentials, were greatest in winter; in contrast, overlap with high mineral potential was greatest during the growing season. We recommend a quantitative and visual GIS approach to scenario planning in the Muskwa-Kechika to maintain the abundance and diversity of wildlife populations there. Resource development would likely increase early seral habitats, presumably benefiting moose, elk, and wolves, but could adversely affect caribou and grizzly bears through habitat loss and increased access.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".