Bibliographic record
Abstract
Pervious concrete pavement is an environmentally friendly, sustainable paving material for low-volume, low-speed applications. Pervious concrete has been used in warm climates extensively, but its use in freeze–thaw climates has been limited. The Centre for Pavement and Transportation Technology at the University of Waterloo and the Cement Association of Canada, Portland Cement Association, and local Canadian ready-mix producers have partnered to carry out a Canada-wide study to evaluate the performance of pervious concrete. Three test sections have been constructed to date with more planned for the future. The current test sections are located in southern Ontario and southern British Columbia. They are designed to represent all aspects of pervious concrete including but not limited to materials, design mixtures, structural design, potential applications, fresh and cured properties, permeability, environmental strain, filtration capabilities, and maintenance needs and options. A surface distress evaluation form has been developed with information gathered from two of the test areas as well as a literature review. Two test areas have experienced a winter season and showed minimal surface distresses and maintained permeability. Permeability testing is carried out regularly to track changes as well as to evaluate effects of winter maintenance. Instrumentation has been installed at the test areas to track moisture movement throughout the structure, strain caused by environmental conditions, and the filtering capabilities of pervious concrete. The results of this project will be instrumental in understanding the performance and behavior of pervious concrete in the Canadian climate.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".