Permeable Pavement Performance over 3 Years of Monitoring
Bibliographic record
Abstract
A 200 m2 permeable pavement test site was installed along Birkdale Road on Auckland's North Shore. Data from the permeable pavement section and an adjacent conventional asphalt section were collected concurrently in 2006 and 2008. Despite installation on an atypical high slope (6.5–6.8%), relatively impermeable subsoils, and active roadway, overall system performance was exceptional. For the 81 complete storms monitored for hydrology, peak flow, runoff timing and volume compared well to predevelopment conditions. A catchment designed on an LID-basis of controlling frequently occurring events would be well served using permeable pavement. Additional hydrologic control may be needed for design storms greater than 5-yr ARI. Water quality characterization for 4–17 storms (depending on pollutant type) was comparable to or better than typical end-of-pipe devices for TSS, PSD, total and recoverable Cu and Zn, and dissolved Cu and Zn. The permeable pavement discharge water quality had consistent event mean concentrations which were statistically lower than the conventional asphalt. Pollutant removal efficiencies are presented. A properly designed permeable pavement section would likely provide adequate treatment for an expanded source area. Permeable pavements should be given strong consideration as an LID at-source control.
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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 | 0.000 | 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".