Effects of adding old shingles to granular material and hot mix asphalt : laboratory and field investigation
Bibliographic record
Abstract
This thesis evaluates the potential use of processed tear-off shingles in road works. Six types of granular materials were investigated to determine the type of material that benefitted the most from using the shingles. The effects of shingles on the stability, as measured by California Bearing Ratio, were found to depend on properties such as gradation and fines content. In general shingles enhanced the stability of materials of relatively low CBR, but decreased the stability of angular well graded material of CBR larger than 100%. Optimum amount of shingles were found to enhance the resistance of stabilized granular materials to cycles of freezing and thawing; however, amounts higher than optimum decreased the resistance to freezing and thawing. In terms of permeability, the addition of shingles did not have a significant effect on the drainage characteristics of the tested materials. A trial road was constructed and showed that after one week of construction dust generated by the control section was found to be twice the amount of dust generated by the shingle section.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".