Behaviour and Performance of Pervious Concrete Pavement in Canada
Bibliographic record
Abstract
Pervious concrete offers sustainable and environmental benefits for stormwater management and urban development. Pervious concrete can be used in low volume, low speed applications in urban and rural settings without creating impermeable space. Therefore it is a low impact development that does not put additional demand on the stormwater management system and in some applications will lower the demand on the stormwater management system. Recent use of pervious concrete in Canada includes four test areas that were constructed to evaluate the performance of pervious concrete in a freeze-thaw climate. The test areas are monitored regularly through surface distress evaluations, permeability and light weight deflectometer testing. Laboratory testing is carried out to evaluate performance in accelerated freeze-thaw testing, compressive and flexural strength, void content and permeability. Two of the test areas are instrumented with moisture sensors and static strain gauges. The data from the instrumentation is available in real time which allows for efficient comparisons and evaluations. The ability for pervious concrete to drain moisture from the surface is measured through permeability testing and development of distresses on the surface are noted in surface distress evaluations. This paper includes the findings to date from the various field and laboratory testings and the data collected from the instrumentation.
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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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.001 |
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".