Current State of the Pavement Warranty in the United States and Canada
Bibliographic record
Abstract
Pavement warranties are implemented by many states as a way to enhance pavement performance, reduce agency costs, and preserve pavement construction. This paper discusses the results of an online questionnaire that was conducted by Jackson State University to review the recent state of practice of warranty specifications in the United States and Canada. From the 34 states that responded to the questionnaire, Florida, Illinois, Indiana, Louisiana, Mississippi, Pennsylvania, and Wisconsin from the United States and British Columbia and Nova Scotia from Canada have pavement warranties on projects. The rest of the states are not planning to adopt pavement warranties in the near future. Although the literature showed some warranty projects in Maine, Minnesota, and New Mexico, they currently do not have pavement warranties on new projects. The paper summarizes and updates pavement warranty information in the United States and Canada based on the states participating in the questionnaire. Although individual state departments of transportation (DOTs) developed their own warranty specifications, which varied in terms of warranty type, warranty period, warranty items, performance evaluation method, etc., similarities are found in the sequence of warranty procedures and components that make up the pavement warranty program.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".