Hydrologic Performance of Three Partial-Infiltration Permeable Pavements in a Cold Climate over Low Permeability Soil
Bibliographic record
Abstract
The hydrologic performance of three partial-infiltration permeable pavement (PP) systems was evaluated at the Kortright Centre for Conservation in Vaughan, Ontario, Canada over 22 months. Partial-infiltration systems allow some stormwater to infiltrate into native soils and drain excess water by way of underdrains. The native soils at Kortright are composed of clayey silt and silty clay till, with clay content ranging from 7 to 30%. Flow restrictors on the underdrains were adjusted to the smallest orifice possible to assess the potential for stormwater outflow volume reductions. The hydraulic behavior of the PP systems was compared with runoff from an asphalt parking lot control. Peak outflow rates from PP were 91% smaller than peak flowrates of asphalt runoff on average, and attenuation of stormwater was observed during all seasons. Stormwater was found to infiltrate at the surface of the PP systems throughout two winters. Although increases in outflow were observed during periods of seasonal thawing due to the delayed release of infiltrating stormwater, the PP systems (with restricted flows from the underdrains) reduced overall stormwater outflow volume by 43% and completely captured (i.e., infiltrated and evaporated) most rainfall events that were less than 7 mm in depth. The study demonstrated that in cold climates and over low permeability soils, partial-infiltration permeable pavements reduce the volume, peak flow, and frequency of storm flows. These changes to the hydrology of stormwater are important for achieving water quality benefits as well as sustaining a more natural water balance and flow regime.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".