Impacts of variations in snow cover on permafrost stability, including simulated snow management, Dempster Highway, Peel Plateau, Northwest Territories
Bibliographic record
Abstract
Permafrost conditions were examined near the Dempster Highway embankment on Peel Plateau, Northwest Territories. Ground temperatures were recorded in 2013–2015 at five sites at the embankment toe and at two sites in undisturbed (control) tundra. Annual mean ground temperatures at approximately 5 m depth ranged from −2.2 to 0.0 °C at the embankment toe and were −1.8 and −2.6 °C at control sites. Permafrost is degrading beside the road at four of five sites. Thaw depths are greater at the embankment toe, where deep snow accumulates, than in undisturbed tundra. A numerical model was used to examine the influence of varying snow cover properties on the ground thermal regime. Simulations indicated that delaying the onset of deep (1 m) snow accumulation and (or) prolonging the duration of the same total accumulation accelerates removal of latent heat from the active layer, increases sensible ground cooling, and results in reduced thaw depth. Furthermore, reducing snow depth and increasing snow density may rapidly raise the permafrost table, lower ground temperatures at the embankment toe, and cool permafrost at depth over several years. In consequence, mechanical snow removal and (or) compaction should be investigated as an active management strategy for mitigating permafrost degradation in ice-rich settings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".