Standard operating procedures for the site selection, design, and maintenance of all-season roads linear infrastructure
Bibliographic record
Abstract
Planning, designing, constructing, and maintaining all-season roads in cold regions requires navigating complex environmental, hydrologic, and geomorphologic challenges. Harsh conditions, such as permafrost, frost-susceptible soils, muskeg, and extreme cold, are compounded by limited data availability, remote locations, and fragile ecosystems. Recent Arctic strategies across the DoD have identified changing weather patterns as significant threats to infrastructure, operations, and training land management in these regions. Key risks include degrading permafrost, changes in precipitation intensity and duration, and the loss of soil bearing capacity in saturated soils, all of which are examined in detail. As the Department of Defense expands Arctic training capabilities, infrastructure investments across Alaska must balance operational goals with sustainability and resilience. This report synthesizes the military’s typical methods for constructing roads in contingency environments, identifies practices used in the construction of the Alaska-Canada Highway, and draws from consultations with training land managers, a thorough literature review, and active engineering research. The report highlights the importance of holistic design that prioritizes longevity, environmental conservation, and safety by addressing cold-region challenges, mitigation strategies, and best practices. This resource is indispensable for military units and infrastructure planners tasked with navigating the complexities of cold-region infrastructure construction and operation.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.096 |
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".