SURFACE REQUIREMENTS FOR BITUMINOUS-AGGREGATE COMBINATIONS
Bibliographic record
Abstract
Bituminous pavement surfaces must meet special requirements for riding comfort, frictional characteristics, permeability, tire and pavement wear, segregation, raveling, appearance, light reflectance, and noise. The focus of this paper is on the progress the asphalt industry has made to date in addressing these special requirements and the challenges it faces in the future. STATE OF THE ART A major change in the asphalt industry in North America is the growing use of the Superpave system for material selection and design of asphalt mixtures. Building on existing knowledge, this system adds new test methods, techniques, and models. The first Superpave pavements were constructed in 1992 and 1993; thus the experience with these new mixtures and their performance is limited. Implementation of the new design system is growing rapidly, with more than 1,300 projects completed in 1998. Although Superpave dominates asphalt technology in the United States and Canada, in other parts of the world, other technologies have continued to gain interest. One exampl is the concept of designing and using asphalt mixtures for specific tasks. Although this is not
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".