Pavement Preservation - Effective Way of Dealing with Scarce Maintenance Budget
Bibliographic record
Abstract
Pavement preservation involves minimizing the destructive impact of climate and traffic by the regular or intermittent timely application of remedial treatments to the pavement. Pavement preservation system should include: pavement management system (PMS); long-term network planning; optimization; cost-effective decision making; and sustainable financing. Objective measurement of pavement performance is required to determine the appropriate treatment. Preventive treatment of asphalt pavements used in Ontario include: crack sealing; crack filling; fog seals and rejuvenating seals; chip seals; slurry seal; cape seal; microsurfacing; non-structural HMA overlay; surface milling and non-structural overlay; cold in-place surface recycling; and hot in-place HMA recycling. Emerging technologies include Nova Chip and Metro MatTM, for instance. This paper first discusses the traditional mindset of many road authorities and how it cannot handle the current needs of road users and the growing concerns of scarce maintenance budget. Next, the concept of pavement preservation is introduced, as well as what separates it from common preventive maintenance practices. A short review of the current preventive treatments used in Ontario is then provided. Examples of successful pavement preservation adopted by municipalities and road authorities in Ontario and in the US are also given. The paper concludes by discussing the issue of how road authorities can move forward with this correct approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".