Conceptual Design of Quantitative Risk Algorithms for a Geohazard and Geo-Asset Management System for Roadway Networks in Permafrost Regions
Bibliographic record
Abstract
Within the Northwest Territories, Canada, permafrost is ubiquitous with communities and industrial development requiring transportation infrastructure (e.g., roadways, airports, and railways) or other linear infrastructure (e.g., pipelines and transmission lines) to accommodate potentially unstable subgrades. In these regions, transportation is of vital social, economic, and political importance. However, warming climate conditions are and will impact the integrity of existing and future transportation infrastructure constructed in permafrost regions. Existing analytical frameworks and tools for geohazard and geo-asset management have not included hazards derived from permafrost; where the ubiquity of permafrost results in the hazard being nearly ever-present underlying the infrastructure and ever changing as climate warming continues through time. This paper presents the conceptual design of quantitative risk algorithms for a geohazard assessment system using dynamic segmentation techniques to discretize the infrastructure spatially based on credibility factors for individual dangers specific to permafrost and changing thermal conditions. The hazard (probability of occurrence) will be calculated based on current conditions and projected using IPCC climate projects for the infrastructure region. This discretized approach will allow infrastructure owners to determine current high-risk areas as well as projections for high-risk areas in the future allowing for more efficient infrastructure improvement planning and an overall increase in roadway network safety.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".