Mechanical properties of unbound limestone aggregates replaced by recycled glass aggregate for pavement in Canada/Quebec
Bibliographic record
Abstract
This study investigated the use of recycled glass aggregate (RG) as unbound base/subbase material. The experimental tests included compaction, Los Angeles, Micro-Deval, and California bearing ratio (CBR) tests. In this regard, a fine proportion of course limestone aggregate (MG20) was replaced by RG with size ranging from 0 to 5 mm based on the volumetric method. Adding RG to the coarse aggregate improved durability in wet conditions, which is more representative of the field condition of Canada/Quebec, but decreased in dry conditions. CBR values decreased with increasing RG inclusion, but all blends with 0%–100% RG in the fine fraction of MG20 met minimum requirements for unbound granular layers in Quebec. A simple model predicting the resilient modulus values of these materials based on CBR values at different stress levels was suggested. This equation estimates Mr values of various aggregate-RG blends under a wide range of mean stresses based on their CBR values.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".