Cold In-Place Recycling - A New Asphalt Pavement Rehabilitation Approach for the City of Calgary
Bibliographic record
Abstract
Cold In-place Recycling (CIR) of deteriorated asphalt pavements is not a new idea for many agencies. However the City of Calgary experienced its first CIR project in 2011. CIR was utilized on 144th Avenue in the northwest quadrant of the city for a distance of about 5.5 kilometres of two-lane roadway with a rural cross-section. This roadway runs east-west between a few good gravel pits and therefore is subject to very heavy truck traffic. The road was exhibiting extensive and extreme fatigue cracking with some localized failures. Coring and drilling was completed to determine the properties of the existing pavement for proper design. This paper discusses the results of Falling Weight Deflectometer (FWD) testing and an International Roughness Index (IRI) survey carried out on the road before and after completion of the CIR, as well as quality control and quality assurance testing. The paper also presents the CIR mix design, and results from a Ground Penetrating Radar (GPR) survey after completion of the project. The results indicated improvement in the pavement modulus and smoothness after construction. The road is performing very well after being in service for one year. (A)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".