Quantifying the rate of track subsidence on permafrost by inferring absolute surface profile from track geometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.002 | 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 it