Railway Geohazards focusing on Sensitive Clay and GAM addressing Climate Change
Bibliographic record
Abstract
Abstract Railway geohazards present unique challenges due to geotechnical factors affecting linear transportation infrastructures such as railways and pipelines, including construction, operations, reliability, and safety. Natural forces and geological processes continuously alter the earth’s surface, posing evolving risks to both existing and newly constructed tracks over time, such as active erosion, rockfalls, and landslides. The worsening effects of climate change are expected to increase the severity and frequency of these natural events. Champlain Sea glaciomarine deposits, in particular, pose significant risks due to their strain sensitivity to ground disturbances. To mitigate this risk category, a geoscientific large-scale overview of geomorphology is essential for assessing the vulnerability of existing tracks and for selecting new routes to ensure safe operation, especially in areas with sensitive clays. To ensure sustainable operational performance throughout the design life of railway or pipeline infrastructures, it is crucial to consider the impact of climate deterioration on natural events. Geotechnical assets, including protective measures, must be rigorously designed beyond typical design codes, as actual codes are often insufficient to meet the forthcoming challenges. This study focuses on scenarios highlighting the risks posed by landslide hazards on linear infrastructures within sensitive clays and emphasizes the necessity of a comprehensive risk management program that integrates climate-adapted mitigation measures as part of a long-term geotechnical asset management program (GAM).
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".