Utilizing Vegetation Indices as a Proxy to Characterize the Stability of a Railway Embankment in a Permafrost Region
Bibliographic record
Abstract
Degrading permafrost conditions around the world are posing stability issues for infrastructure constructed on them. Railway lines have exceptionally low tolerances for differential settlements associated with permafrost degradation due to the potential for train derailments. Railway owners with tracks in permafrost regions therefore make it a priority to identify potential settlement locations so that proper maintenance or embankment stabilization measures can be applied to ensure smooth and safe operations. The extensive discontinuous permafrost zone along the Hudson Bay Railway (HBR) in Northern Manitoba, Canada, has been experiencing accelerated deterioration, resulting in differential settlements that necessitate continuous annual maintenance to avoid slow orders and operational interruptions. This paper seeks to characterize the different permafrost degradation susceptibilities present at the study site. Track geometry exceptions were compared against remotely sensed vegetation indices to establish a relationship between track quality and vegetation density. This relationship was used as a proxy for subsurface condition verified by electrical resistivity tomography. The established relationship was then used to develop a three-level degradation susceptibility chart to indicate low, moderate and high susceptibility regions. The defined susceptibility regions can be used to better allocate the limited maintenance resources and also help inform potentially long-term stabilization measures for the severely affected sections.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".