Bibliographic record
Abstract
This synthesis report will be of interest to construction, maintenance, pavement design, and materials engineers, pavement contractors, and others interested in the use of open-graded friction course (OGFC) mixes. It describes the current state-of-the-practice on the use of OGFC mixes. This includes information regarding design, materials, construction, maintenance, and rehabilitation strategies. Alternative treatments to traditional OGFC are also identified and discussed. Information for the synthesis was collected by surveying U.S. and Canadian transportation agencies and by conducting a literature search to gather further information on North American and European practices. This report of the Transportation Research Board describes the recent performance of North American OGFC and European porous asphalt by identifying and discussing benefits and stress indicators. A new generation of OGFC has evolved in the last five years with changes that have been reported to dramatically improve the performance of OGFCs. Changes include a combination of empirical design adjustments, adoption of innovative technologies, and improved methods of construction. The synthesis describes new material and design methods in use, as well as the applicability of the new generation of open-graded mixtures to North American use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".