PermaRail: a transdisciplinary approach to increase railway resilience to degrading permafrost terrain under a warming climate
Bibliographic record
Abstract
Across the Canadian permafrost zones, access to stable linear infrastructure networks is critical for the well-being of Northern Communities and the Canadian economy.The Hudson Bay Railway (HBR), the first major transportation infrastructure built over permafrost in Canada, is now facing significant climate-driven stability and drainage issues that have been exacerbated by climate change.These issues increase maintenance costs and threaten user safety.The focus of this transdisciplinary project is to identify and characterize permafrost-related hazards along the railway corridor and investigate potential mitigation measures for improving rail stability and minimizing risk.A multi-year comprehensive field program is currently underway to map, characterize, assess, and monitor ground and rail conditions, including permafrost, ice, soil, and surface water conditions.This field program, supplemented with experimental and numerical analyses at targeted pilot sites, will provide the basis to identify high-risk locations and support a quantitative assessment of mitigation and adaptation options under different climate scenarios.By developing a risk-based framework to assess degrading permafrost-related hazards, as well as design and mitigation best practices for railways, the long-term goal of this project is to improve the resilience, sustainability, performance, and safety of the Hudson Bay Railway.This project aims to address the unique transportation needs and priorities of specific communities in Northern Manitoba and contribute to Canadian expertise and leadership in the management of current and future linear infrastructure in permafrost regions around the world. 1
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".