Quantifying the rate of track subsidence on permafrost by inferring absolute surface profile from track geometry
Bibliographic record
Abstract
The Hudson Bay Railway in Northern Canada traverses over a thousand kilometers of challenging ground conditions, including peatlands, discontinuous permafrost, and continuous permafrost. Monitoring climate change impacts to the rail corridor is challenging, as ground conditions are changing rapidly, and access to remote locations is limited. As a result, the unknown rate of thaw settlement hampers quantitatively-informed maintenance and rehabilitation strategies. In this manuscript, we evaluate a workflow to transform normalized track geometry measurements (in the form of Surface 62) into absolute profiles of track surface elevation. Field validation of the method was undertaken at three strategically chosen field sites: Site A, a simple isolated subsidence feature located at the transition zone between a low-lying fen and a peat plateau; Site B, a more complex subsidence feature exhibiting clear signs of an advanced stage of permafrost degradation; and Site C, a bridge crossing experiencing track heave due to frost jacking of pile foundations. Validation of the method against LiDAR measurements illustrates that peak depth or heave of a feature can be estimated within 6 %. Furthermore, given the high temporal resolution of the track geometry measurements, this method can capture the rate of thaw subsidence and track settlement. This rate, observed to be 0.26 mm/day at Site A, illustrates the enormous challenge posed to infrastructure owners tasked with maintaining track geometry in permafrost environments under a changing climate.
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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) | 0.001 | 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".