Evaluation of the Use of Green Pavement Technologies in the Region of Waterloo, Ontario
Bibliographic record
Abstract
The Region of Waterloo (Region) has a long history of applying innovative pavement technologies. The engineering staff at the Region realized that the conventionally used pavement resurfacing, reconstruction or mill and overlay are often not cost effective and environmentally friendly solutions and decided to use innovative, green technologies. The following pavement rehabilitation technologies are commonly used by the Region now: Cold In-place Recycling (CIR); Full Depth Reclamation (FDR) using expanded asphalt; pulverizing the existing pavement and resurfacing; and Cold In-Place Recycling with Expanded Asphalt Material (CIREAM). The Region is also looking forward to the application of Hot In-place Recycling (HIR) that is coming back to Ontario. Pavement preservation technologies have also been used and include crack sealing, microsurfacing, bonded wearing course and thin overlays. The Region has used reclaimed RAP in the HMA mixes for a long time. Warm Mix Asphalt has been tried and is being considered in future works. Being aware that the most sustainable pavements are the ones that last the longest, the Region has focused on proper longitudinal joint construction using echelon paving, infrared heaters and are planning the use WMA in the future.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".