Snow depth as a function of canopy cover and other site attributes in a forested ungulate winter range in southeast British Columbia
Bibliographic record
Abstract
Snow depth is considered a major influence on deer (Odocoileus spp.) winter distribution and abundance in northern parts of their range. Overstorey canopy cover is often considered a principal variable governing snow depths in forests and has implications for managers who wish to achieve reduced snow depths by manipulating canopy closure in forests. I used three years of snow-depth data collected in forested ungulate winter range in southeast British Columbia to determine the relative influence of canopy closure and other site attributes on snow depth. Although canopy closure was a major factor in determining snow depth, it was outweighed by elevation and aspect. I found a close relationship between canopy closure and snow depth at low-elevation sites, but this relationship diminished or disappeared at higher elevations and on cooler aspects supporting the hypothesis that the influence of canopy closure depends on overall snow accumulation. At low elevations, forest managers could use canopy closure to influence snow depths. I offer the generalization that, on similar sites, maintaining 50% canopy closure will reduce snow depths by approximately 20%; 100% canopy closure will reduce snow depths by up to 40%.
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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) | 0.000 | 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".