Low-Impact-Development Practices for Stormwater: Implications for Urban Hydrology
Bibliographic record
Abstract
Since 1970, the design focus of urban stormwater systems has expanded from quick removal of stormwater to include control of peak flows (1970s) and removal of pollutants (1980s). The addition of stormwater ponds as control elements satisfied these two concerns to some extent, but further concerns arose in the 1990s related to changes in flow patterns in urban receiving waters, such as extended periods of high flow rates and reduced baseflow. These changes result from altered surface water storage, reduced evapotranspiration and infiltration, and increased runoff in urbanized areas and cause unintended damage to ecosystems dependent on surface water and groundwater. An alternative form of urban development and stormwater management, called Low Impact Development (LID), provides for urban development while maintaining hydrologic and water quality characteristics closer to those existing prior to urbanization. This study used watershed modelling to evaluate the capability of LID techniques to mitigate the impact of urbanization on hydrology using a catchment area in Kitchener, Ontario as a case study. Results are consistent with those reported in recently published papers and demonstrate that LID practices have the potential to minimize the undesirable hydrologic effects of urbanization not only in new developments but also in a retrofit application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".