Low-Impact-Development Practices for Stormwater: Implications for Urban Hydrology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it