Effect of climate change on frost penetration depth in the subgrade soil beneath railway tracks: case study
Bibliographic record
Abstract
Frost heave along Canadian railways is a common issue that can result in track geometry issues and reductions in train speeds and rail traffic capacity. This study was conducted at a location prone to frost heave on VIA Rail Canada’s Smith Falls subdivision to determine the maximum frost penetration depth at this site during the last 20 years and explore a correlation between frost depth and weather conditions for future anticipation of frost depth. The results include frost depth fluctuations over the last 20 winters using a thermal analysis conducted by TEMP/W, a regression between weather data and frost penetration depth, and predicted frost depth for the next 75 years based on a high greenhouse gas climate model. The results showed that frost depths estimated using conventional methods and numerical analysis were significantly different and the maximum frost depth tends to decrease in the future but it will be within the frost susceptible layers and could still be problematic at this site. So further monitoring or actions might be needed to prevent frost heave and consequent thaw softening at this location.
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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 | 0.001 | 0.001 |
| 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".