Modeling Green Infrastructure Components in a Combined Sewer Area
Bibliographic record
Abstract
The purpose of the project described in this chapter is to evaluate the water quality and quantity improvement benefits of a large scale application of green infrastructure control practice retrofits in an entire monitored subcatchment.These green infrastructure controls have been shown, when implemented and maintained properly, to increase retention at the runoff source.This increased retention decreases the runoff volume entering the drainage system and the demand on a drainage system.Many researchers have reported findings that support these observations for individual or small neighborhood applications at LID (low impact development) conferences.This project is unique in that a large area is being retrofitted and will be monitored for many different scales to measure these benefits.This chapter describes a preliminary modeling effort that is being used to assist in the design of the practices at the site, showing how complementary practices that can be constructed on private property which will enhance the performance of the curb-side biofilters to be constructed in the public right-of-way.This chapter describes updated modeling results for the use of rain gardens, rain barrels or tanks, and roof disconnections, together with preliminary calculations pertaining to curb cut biofilters.They are being examined for potential application in the Kansas City, Missouri, test area for the control of combined sewer overflows.The initial modeling results using WinSLAMM indicate that the use of bioretention facilities in the test area (which has poor soils with limited infiltration capacities) can be effective in
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".