Bibliographic record
Abstract
This paper covers the development and use of Roller Compacted Concrete (RCC) pavements in Canada since its inception in 1976. The application of RCC in the pavement market has undergone a major expansion and transition since its introduction to the forestry industry in Western Canada (1). Unlike other traditional paving materials, RCC has proven itself as a superior structural pavement in the first instance and is now gaining favour with roadway agencies in the more common municipal arena. Along with specialty applications such as RCC inlays for intersections (Fast-Track RCC!), designers have also used this innovative construction technique for subdivision residential and arterial roadways (2). Examples exist where RCC has been used exclusively for reconstruction of truck lanes (3) on major routes as well as alleys for garbage collection. The first RCC overlay of an existing deteriorated asphalt parking area was completed in 1996. A brief review of RCC as a material will be presented. Construction methodology common to Canada will be covered and alternative construction equipment options will be suggested (4). Example projects will form the main body of the paper. Early applications from the resource based industries in Canada such as forestry, pulp and paper and composting (5) are used to lead into the transition RCC pavement is experiencing. A broad cross-section of roadway applications and commercial uses of RCC are presented to effectively illustrate the emergence of RCC paving as a viable option for today and the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".