The Effectiveness of Centralized versus Decentralized Green Infrastructure in Improving Water Quality and Reducing Flooding at the Catchment Scale
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
Green infrastructure (GI), such as green roofs, rain gardens, and porous pavement, is a stormwater management strategy designed to capture rain where it falls and allow it to soak into the ground rather than running off into a stream channel, thus reducing flooding and improving water quality. While there has been a lot of research into the performance of individual GI projects, much less is known about its performance at the catchment scale. This study uses a US EPA SWMM model to examine the effectiveness of GI in improving water quality and reducing flooding at the catchment scale. Results show that in the study catchment, a large centralized wetland was the most effective at reducing and slowing peak discharge. Infiltration based decentralized GI best reduced flood volumes. In addition to changes in effective impervious area, flood volumes were also reduced due to differences in drainage network structure and modifications to the pervious portions of the catchment. Reductions in flood volumes resulted in lower pollutant loads, except for pollutants that are particularly efficiently removed by wetlands. Routing runoff through a large, centralized wetland removed more nitrate load than letting rain infiltrate where it falls.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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