Assessing the Potential For Rehabilitation of Surface Permeability Using Regenerative Air and Vacuum Sweeping Trucks
Bibliographic record
Abstract
Permeable pavements (PPs) have been used as stormwater management systems throughout Ontario for over twenty years.After years of sediment and debris buildup, surface clogging reduces the infiltration of stormwater and inhibits the hydraulic and environmental functions of the pavement.Removal of surface material has been shown to restore infiltration but the majority of studies have been limited to small scale testing.This chapter presents the results and experiences of the first testing of regenerative air and vacuum sweeping trucks on permeable pavements in Ontario, which was conducted in 2011.A regenerative air truck was tested on two parking lots with well used permeable interlocking concrete pavers and pervious concrete, while a vacuum sweeping truck was demonstrated on a third parking lot with permeable interlocking pavers.Both systems proved to provide partial rehabilitation of the permeable pavements.Post treatment surface infiltration rates on all three parking lots displayed large spatial variability, showing that micro-conditions throughout the pavement have a confounding influence on the overall effectiveness of maintenance.The impact of maintenance may be improved by establishing regular cleaning intervals and developing instructional guidelines for pavement owners and equipment operators.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".