Winter habitat use by mountain goats in the Kingcome River drainage of coastal British Columbia
Bibliographic record
Abstract
Using radio telemetry from September 1994 to March 1996, we studied the winter habitat use of 15 mountain goats in the Kingcome River drainage on the south coast of British Columbia, Canada. Our objectives were to identify important attributes of coastal mountain goat winter habitat and, in doing so, to provide resource managers with information that will help them make decisions about conserving and managing goat habitat in coastal British Columbia. We used a digital elevation model, Terrestrial Ecosystem Mapping, and Vegetation Resource Inventory mapping with a Geographic Information System to determine selection by 13 female mountain goats for forested site series and other habitat variables at two different scales. At both scales of selection, mountain goats chose southerly aspects (110–250°) and western hemlock-leading forests greater than 250 years in age, but we observed no evidence for site series preference. Most goat locations were within 150 m distance of rock-outcrop polygons. Depending on the scale of selection analyses, goats selected elevations from 600–1200 m, slopes from 41 to 60°, and the Montane Very Wet Maritime Coastal Western Hemlock (CWHvm2) or Windward Moist Maritime Mountain Hemlock (MHmm1) subzone variants. Goats selected moderate classes of forest volume and crown closure, and sites with shrub cover 1–2 m in height. These attributes are likely associated both with lower snow depths and higher amounts of available forage for goats. Our study shows that it is important for managers to assess whether planned harvests conflict with goat winter habitat. Although the harvestable area on the coast that overlaps with goat winter habitat may not be large, some of these habitats could be very important for goats, particularly during deep snow periods.
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 | 0.000 | 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 | 0.000 |
| 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".