Investigating the Effects of Groundwater Flow on the Thermal Stability of Embankments over Permafrost
Bibliographic record
Abstract
Degrading permafrost below roadway embankments is a widespread problem in the north. Thermal modeling can help to determine thermally stable embankment configurations; however, this modeling typically does not include the effects of groundwater flow on the geosystem, which will cause permafrost degradation to occur faster than atmospheric warming alone. As part of a larger, ongoing research project, we present preliminary results of heat transfer modeling for an Alaska Highway test section near Beaver Creek, Yukon Territory, Canada. This experimental highway test section is located in an area characterized by muskeg vegetation underlain by ice-rich permafrost. While the overall project includes field work and laboratory measurements, this paper focuses on the results of exploratory modeling. A two-dimensional finite element program capable of mathematically coupling both heat and groundwater flow was used for modeling. Comparing model results produced using conductive heat flow only to model results using both heat and groundwater flow (heat advection) indicates that groundwater has a significant effect on the configuration of the thaw bulb and the temperature distribution within and below the roadway embankment. For example, using 50-year model results, including groundwater flow increases modeled thaw depths below the embankment by approximately 8 m, and can increase temperatures to an excess of +2.5°C at the bottom of the embankment. For wet terrain conditions, these preliminary modeling results indicate that it is essential to incorporate groundwater flow into thermal modeling, in order to understand better the complex interactions between roadway embankments and underlying permafrost.
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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 | 0.000 | 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".