Guidelines for Development and Management of Transportation Infrastructure in Permafrost Regions
Bibliographic record
Abstract
Permafrost underlies about half of the landmass of Canada and our northern road, rail and air transportation system is reliant on the strength of permafrost soils. Defined as a ground condition of either soil or rock that remains at or below 0 degrees C for long periods, permafrost is often a very capable foundation but it is very sensitive to changes in thermal conditions. Traditional planning, design, construction and maintenance practices are often poorly adapted to permafrost conditions, and climate change is adding uncertainty to performance prediction. This guide provides a compendium of best practices for development, planning, design, construction management, maintenance and rehabilitation of transportation facilities in regions of northern Canada with permafrost terrain. It is intended to be a practical, easy-to-read guide for those directly involved in any aspect of the life cycle of infrastructure in northern Canada. This guide is intended for specific use by project managers and planning/design engineers as well as maintenance personnel in their day to day work. It is also intended as a general reference for senior management to gain an understanding of the challenges of developing and managing transportation infrastructure in permafrost regions.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".