Surface energy balance of sub‐Arctic roads with varying snow regimes and properties in permafrost regions
Bibliographic record
Abstract
Abstract Surface energy balance (SEB) strongly influences the thermal state of permafrost, cryohydrological processes, and infrastructure stability. Road construction and snow accumulation affect the energy balance of underlying permafrost. Herein, we use an experimental road section of the Alaska Highway to develop a SEB model to quantify the surface energy components and ground surface temperature (GST) for different land cover types with varying snow regimes and properties. Simulated and measured ground temperatures are in good agreement, and our results show that the quantity of heat entering the embankment center and slope is mainly controlled by net radiation, and less by the sensible heat flux. In spring, lateral heat flux from the embankment center leads to earlier disappearance of snowpack on the embankment slope. In winter, the insulation created by the snow cover on the embankment slope reduces heat loss by a factor of three compared with the embankment center where the snow is plowed. The surface temperature offsets are 5.0°C and 7.8°C for the embankment center and slope, respectively. Furthermore, the heat flux released on the embankment slope exponentially decreases with increasing snow depth, and linearly decreases with earlier snow cover in fall and shorter snow‐covered period in spring.
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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 | 0.000 | 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".