Development of a Sustainable Road Surfacing Policy for Provincial Highways in New Brunswick
Bibliographic record
Abstract
The New Brunswick Department of Transportation and Infrastructure is responsible for the rehabilitation and maintenance of approximately 19,650 km of provincially designated highways and roads. Nearly 85 percent of this network is hard surfaced including 9800 km of designated local highways and roads. Maintaining this local network at an acceptable standard requires a significant investment that has become increasingly challenging given current economic and fiscal constraints. Available funding is most often directed towards higher priority projects, while many low volume local asphalt roads are deteriorating with escalating rehabilitation costs. The Department's Long Term Investment Projection estimates an average of 300 km of asphalt rehabilitation is required annually over the next ten years to sustain the paved highway network. However, New Brunswick like other the provinces is implementing measures to reduce annual deficits and achieve spending efficiencies. In response, the Department undertook a study to develop a policy to ensure that the most appropriate surface type is selected in the future based on clearly defined and transparent level of service criteria that considered engineering suitability, least life-cycle cost, and functional / service characteristics. This paper documents the development of a multi-staged, screening pavement preservation decision making framework that will support the province to achieve a stronger position for achieving infrastructure sustainability. (A) For the covering asbtract of this conference see ITRD record number 201211RT334E.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".