Lessons learned from long-term frost heave monitoring under a railway embankment
Bibliographic record
Abstract
Frost heave is a major issue for railway tracks constructed in cold regions that degrades track geometry and may affect the safety of railway operations. Canadian railway operators perform frequent maintenance during winter to eliminate track deformation and ensure the safe passage of trains. In early spring, frequent maintenance such as tamping and surfacing are required to alleviate the surface deformation due to thawing. Freeze-thaw cycles are expected to become more frequent under future climate conditions and thus a greater understanding of this phenomenon is essential to develop adequate mitigation measures in the face of a changing climate. National Research Council Canada in collaboration with VIA Rail Canada has conducted a 3-year field investigation to study the mechanism of frost development and its impact on safety and performance of train operations. In this project, a 50 m section of track in eastern Ontario was instrumented with various geotechnical and structural monitoring systems. In addition, measurements from ground penetrating radar and a track geometry car were collected over 90 km of track to map frost-susceptible sections of track and quantify its effect on track geometry degradation. The difference in winter conditions during the monitoring period (which consisted of two freeze-thaw seasons) in terms of temperature and snow on ground, induced different temperature regimes within the track substructure and led to different track responses. This paper summarizes some of the major lessons learned during the field observation period and discusses how the expected future climate may adversely affect the frost heave issues.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".