Surface Infiltration Rate of Permeable Pavements
Bibliographic record
Abstract
Asphalt surfaces have greatly increased the amount of pollutant-carrying runoff entering surface waters.To counteract this, permeable pavement can be installed to allow water to infiltrate, thus reducing runoff and acting as a filter.This study tested the surface infiltration rate of 27 permeable pavement sites in North Carolina, Maryland and Delaware.One of these sites in North Carolina was monitored to compare pollutant loads of asphalt runoff to those in infiltrate.Concrete grid pavers (CGP) and permeable interlocking concrete pavers (PICP) were tested with pavement ages ranging from six months to 20 years.Two infiltration tests were run on 14 CGP lots filled with sand.The initial test was on the existing condition of the surface and second test was run after the removal of the top layer of residue (1.3 -1.9 cm) to simulate maintenance.Maintenance improved the infiltration rate on 13 of 14 sites.Analysis of the data showed that maintenance improves surface permeability at a confidence level of 99.8%.The median average infiltration rate increased from 5.0 cm/h, for existing conditions, to 8.0 cm/h after maintenance.Eleven PICP sites were also tested.Sites built in close proximity to loose fine particles had infiltration rates significantly less than sites free of loose fines.Averages of each condition are 60 cm/h and 2000 cm/h respectively.Even the minimum existing infiltration rates were comparable to those of a grassed sandy loam soil.Water quality data included in this study shows the results of six storms from June to October, 2003.With only a few storms to compare,
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".