Modelling critical winter habitat of four ungulate species in the Robson Valley, British Columbia
Bibliographic record
Abstract
A modelling exercise was conducted to identify potential critical winter habitat for four ungulate species in the Robson Valley in east-central British Columbia: mule deer (Odocoileus hemionus hemionus), whitetailed deer (Odocoileus virginianus), Rocky Mountain elk (Cervus elaphus nelsonii), and moose (Alces alces). The model was developed to provide land managers with an effective decision-making tool to include critical winter habitat in land-use planning. Forest cover data, biogeoclimatic data, and a digital elevation model were used to reflect snow depth, forage availability, thermal cover, and security cover values during winter months. The model identifies low-elevation, south-facing, older forests where snowpacks are less deep as potential critical winter habitat for deer and elk. Because moose are better adapted to northern Interior winter conditions, the model identifies coniferous and deciduous stands with greater forage potential. Recent mild winters have limited the field validation process. Habitat assessment, using local sites as benchmarks for model evaluation, found that the distribution of resources varied within and between highrated polygons and that the model overestimates the amount of critical ungulate winter habitat in the Robson Valley. Forage availability, followed by snow interception, were shown to be the limiting factors in most cases. The model is a broad filter of critical ungulate winter habitat; it is intended for field use as a management tool to identify the boundaries of critical winter range. The limitations of the model and priorities for improvement are reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".