Project-Level Highway Management Model for Secondary Highways in Saskatchewan, Canada
Bibliographic record
Abstract
Saskatchewan Highways and Transportation (SHT) is responsible for 26,000 km of primary and secondary highways in the province. The primary system was structurally built to handle high traffic volumes and heavily loaded trucks; the secondary system was constructed to provide links into the primary system for traffic volumes lower than 500 vehicles per day with few heavily loaded trucks. Secondary highways consist mostly of thin membrane surface (TMS) highways, which are oil-treated surfaces over a nonstructural roadbed. In the past few years increased heavy-truck traffic associated with rail line abandonment, elevator closures, and increased truck haul associated with economic development has deteriorated TMS highways. Years of increasing pressures and inadequate funding have forced SHT to develop and implement cost-effective, sustainable methods to manage and preserve them. A new strategy to structurally strengthen the system is a cementitious blend of material called TerraCem. Along with conventional strengthening strategies, this new method is being used throughout Saskatchewan; however, its long-term performance is unknown. Because SHT must make good decisions and should be able to demonstrate they are good, a project-level framework capable of evaluating secondary road management strategies on the basis of whole life-cycle road agency and road user costs, has been developed. The framework determines the lowest-cost strategy (agency and road user) and employs probabilistic modeling to quantify the long-term performance of the TerraCem strategy. The developed framework was applied for a project-level sample.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".