Evaluating the impact of snow fencing on snow conditions and ground temperatures in Hurricane Alley, Dempster Highway, Yukon, Canada
Bibliographic record
Abstract
Snow fences were installed at two sites near the Dempster Highway, Yukon, ~ 10 km south of the territorial border, to examine their impact on snow accumulation and ground temperatures. Temperature sensors were installed in August 2018 and snow surveys were conducted throughout winter 2018‒19. Natural snow accumulation ranged from a shallow snowpack of low density in wind-scoured upland, to high density, deep snow in the lee of a large hill. The snow fences accumulated wind-blown snow in large drifts of high density, which neared capacity by December. Topographic factors were not found to significantly alter drift characteristics at the fences. By late winter, thermal resistance was no greater in snow fence drifts than in natural tundra, however mean winter (Dec.-Feb.) ground temperature was higher beneath snow fence drifts than tundra by 3.5°C at 10 cm depth and 2.7°C at 50 cm depth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".