Bibliographic record
Abstract
This article describes how regional partnerships link state and local agencies with state-of-the-art pavement preservation practice. As road agencies that are strapped for cash look for ways to optimize their limited dollars, many are taking a much closer look at the practice of pavement preservation. And standing there to help is a host of regional partnerships across the United States and Canada that bring together representatives of state and local agencies, contractors, suppliers, academic institutions, consultants and the Federal Highway Administration (FHWA) to promote pavement preservation while advancing research. Pavement preservation techniques are being promoted by the FHWA and the American Association of State Highway & Transportation Officials (AASHTO) as cost-effective and environmentally-sustainable strategies designed to extend the life of existing pavements before they deteriorate substantially. These techniques include nonstructural preventive maintenance surface treatments such as crack sealing, chip sealing, micro surfacing and thin-lift hot-mix asphalt paving. Pavement preservation methods are intended to prolong pavement life, avoiding high future costs of reconstruction or rehabilitation through the expenditure of lesser amounts of money at critical points in a pavement’s life. Pavement preservation pays off in both the short and long term. Experience shows that spending a dollar on pavement preservation can eliminate or delay spending $6 to $10 on future rehabilitation or reconstruction costs. But because pavement preservation techniques must be applied at a critical point, their long-term successful use depends on the timing of the treatment. Pavement preservation dollars also may cannibalize dollars that might be used for capacity improvements or new construction. If done too early, preservation techniques may mean scarce funds will be spent on a pavement not needing the treatment, while denying those funds to pavements that might. Done too late, the pavement will disintegrate even beneath the freshly placed treatment, wasting the effort. Research and education may be needed to implement the right program, and that’s where the regional partnerships are helping transportation agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".