Pavement Rehabilitation with Cold Recycling Technology on Highway 8 (Queenston Road) in the City of Hamilton
Bibliographic record
Abstract
The use of cold recycling technologies for pavement rehabilitation or structured reinforcement is popular among many agencies in central and southwestern Ontario. For suitable roadway candidates, use of cold recycled mixes yields environmental and economic benefits versus traditional surface rehabilitation or reconstruction techniques. The City of Hamilton has limited experience with incorporating recycled cold mix base courses in pavement structures. This paper discusses the design and construction components of the Highway 8 (Queenston Road) rehabilitation project. The project was completed on a 5.3 km section of east-west arterial road in a heavily trafficked urban environment. Partial-depth and full-depth reclamation of the existing pavement structure were selected to construct a recycled cold mix base course. Stabilization of the reclaimed material was achieved by adding expanded (foamed) asphalt binder and overlaying it with 50 mm of hot mix surface course. The impacts of cold recycling technology on traffic, adjacent residents and businesses, and overall budget implications are discussed and compared with conventional rehabilitation strategies. The City of Hamilton will continue to monitor the performance of the pavement to ensure that recycled cold mix base courses provide an acceptable rehabilitation strategy to deal with infrastructure renewal requirements in both an economical and sustainable fashion.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".