Degradation of permafrost beneath a road embankment enhanced by heat advected in groundwater<sup>1</sup>This article is one of a series of papers published in this CJES Special Issue on the theme of <i>Fundamental and applied research on permafrost in Canada</i>.
Bibliographic record
Abstract
For the past few decades, northwestern North America has been affected by climate warming, leading to permafrost degradation and instability of the ground. This is problematic for all infrastructure built on permafrost, especially roads and runways. Thaw settlement and soil consolidation promote embankment subsidence and the development of cracks, potholes, and depressions in road pavement. In this study, we investigate highway stability in permafrost terrain at an experimentally built road embankment near Beaver Creek, Yukon. A network of 25 groundwater monitoring wells was installed along the sides of the road to estimate groundwater flow and its thermal impact on the permafrost beneath the road. Data on topography, water-table elevation, ground temperature, and stratigraphy of the soil were collected at the site. The geotechnical properties of each soil layer were determined by laboratory analysis and used to calibrate a two-dimensional groundwater flow model. Field observations showed that water was progressively losing heat as it flowed under the road embankment. Our results suggest that advective heat transfer related to groundwater flow accelerated permafrost degradation under the road embankment.
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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 | 0.000 | 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".