InSAR Monitoring of Alaska Highway Instability in Permafrost Regions Near Beaver Creek, Yukon
Bibliographic record
Abstract
Abstract Thaw subsidence can damage the infrastructure including buildings, roads and airfields founded on ice- rich permafrost, increase their maintenance costs, change the landscape and influence the sustainable development in the northern region. Information about the ground movements is important for making decisions on various geotechnical approaches to reduce impacts of permafrost degradation. However, field measurements of ground movements and long term monitoring using traditional field survey may be logistically expensive in vast and remote Northern Canada and Alaska, USA. The ability to measure surface displacements, identify the areas being impacted, and provide information of seasonal timing using remote sensing techniques would improve the knowledge and expertise of those involved in infrastructure engineering and management where permafrost is degrading. Traditional Interferometric Synthetic Aperture Radar (InSAR) measurements of deformation do not consider the effects of seasonal freeze-thaw, thus may not effectively reveal the long term trend of ground movements in permafrost region. In this paper we propose to quantitatively evaluate the seasonal ground movements resulted from on-going seasonal freezing and thawing, and estimate long term deformation of linear infrastructure in permafrost area using InSAR technique. The proposed approach has been tested on Alaska Highway built on permafrost at Beaver Creek, Yukon, Canada using Radarsat 2 data acquired during 2013-2015. Results indicate that there was long term deformation at a rate of five cm/year, in addition to an average of magnitude of vertical movement of 4 cm between winter heaving and summer thawing during annual climate cycles.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".