Bibliographic record
Abstract
Rutting of pavement's Asphalt may cause serious problems with increased traffic of heavy vehicles, especially in tropical regions.Increased resistance of asphalt mixtures has always been an important issue in improving the efficiency of road pavement.Various methods for increasing the resistance of asphalt mixtures are presented.In this study, a new material is used to increase the resistance that was not considered before.In sugar producing factories, used lime of the production process remains as waste and abandoned in environment; under the influence of climatic factors (wind, flood) it is transferred to the surrounding environment contaminating agricultural lands and gardens as well as the groundwater.Therefore, efficient use of it (lime waste) as a valuable product in the economic cycle is profitable, thus; the first option in efficiency can be the manufacturing of hot asphalt industry from some additives such as hydrated lime as filler.The possibility of using hydrated waste lime produced in sugar factories instead of the current consumption is the objective of this study.The plan's success will play an important role in the creation of added value.Several field trials were conducted by adding varying amounts of the material as filler in asphalt and product's quality indicators were measured.The results indicate the possibility of replacing the material instead of current ones.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.956 | 0.960 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".