Snow management to reduce ground temperatures beside a road in the boreal forest near Mayo, Yukon
Bibliographic record
Abstract
A field experiment was conducted in winter 2020-21 adjacent to the South McQuesten Road, central Yukon, to determine the effect of snow compaction on ground surface temperatures.At two sites, snow machines were used to compact the snow beside the road from late November to March.Data loggers were placed at the base of the snowpack to record the temperature at compacted and untreated plots at each site.The thickness of snow layers, and their density, hardness, and grain shape and size were recorded in snow pits after each compaction.By March, the snowpack was 35-40 cm thicker at the untreated plots and on average 156 kg m -3 denser at the compacted plots.The depth hoar layer, 10-15 cm thick at the untreated plots, was almost entirely crushed during compaction.Over the winter, daily ground surface temperatures at the compacted plots were 2-3 °C lower on average than at the undisturbed plots.A reduction in annual mean surface temperature of 0.9-1.3°C may be expected due to snowpack compaction.Seasonally, the n-factors at the compacted plots were 0.25 and 0.15 higher than at the undisturbed plots (0.37 and 0.41, respectively).These data indicate the efficacy of a local approach to mitigating ground warming beside highways.1
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".