Framework for Microsurfacing Project Selection: Key to Long-Term Performance
Bibliographic record
Abstract
National Cooperative Highway Research Program (NCHRP) Synthesis 411 found that the most important factor for good long-term performance of microsurfacing used in a typical agency pavement preservation program was project selection. This paper expands on that information and furnishes a decision-making framework for selecting roads whose characteristics make them good candidates for microsurfacing. The tool is based on the output of a survey that included responses from 44 US state and 12 Canadian provincial highway agencies combined with the results of a comprehensive literature review. The paper concludes that microsurfacing is a pavement preservation tool with very few technical or operational limitations that is best suited to correct rutting, raveling, and loss of surface friction. It performs well if it is used to preserve structurally sound pavements. The survey found that it is considered a highly specialized treatment and as a result, most agencies do not understand those conditions in which microsurfacing can accrue significant pavement preservation benefits. Also, few US and Canadian agencies have a formal project selection process Therefore, the framework presented in the paper is both timely and needed. It will furnish agencies with the ability to identify those roads where microsurfacing is the appropriate treatment.
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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 | 0.045 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".