Performance Modelling of Actively Controlled Green Infrastructure Options in a Mixed Use Neighborhood Retrofit
Bibliographic record
Abstract
Green infrastructure (GI) is typically implemented with static flow control devices. More recently, actively controlled GI concepts have been developed (e.g., OptiRTC, RainGrid) and applied to demonstrate that additional benefits (e.g., lower runoff volume, cost savings) can be accomplished with different operating strategies. The objective of this study was to evaluate the potential long-term performance of several actively controlled GI technologies implemented throughout a mixed-land use neighborhood. Detailed USEPA SWMM models of the drainage area and a spreadsheet tool were developed for conventional, GI and controlled GI scenarios, and a separate spreadsheet tool was developed to simulate and test different operating strategies. Model results indicate that the GI implementation results in an overall runoff reduction and peak flow reduction of 54.7% and 26.3%, respectively. Controlled GI offered a small incremental improvement, but was able to maximize the use of existing infrastructure and minimize the negative effects caused by sewer overflows.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".