Bibliographic record
Abstract
The viability of used lubricating oil as an asphalt modifier was studied, with the enhancement of the low-temperature grade as the specific goal. Used oil modification was found to improve the Superpave low-temperature performance grade (PG), but at the expense of the high-temperature PG grade. When evaluated according to the Superpave MP1 specification, the low-temperature grade of the modified asphalt was not significantly improved due to failure of the bending beam rheometer (BBR) test’s m value. When evaluated according to the Superpave MP1a specification, the modified asphalt overall PG grade temperature spread remained essentially constant, varying only by approximately two degrees. The asphalt took as much as 12% of oil and still had less than the maximum limit of 1.0% rolling thin film oven test (RTFOT) mass loss (emissions). However, the oil may possibly have a detrimental effect on the asphalt quality, such as reduced adhesiveness to the aggregates, leading to stripping and raveling. The field performance test should be checked before considering lubricating oil as a modifier.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".