R4: Regulatory Runoff Reduction in the Right-of-Way
Bibliographic record
Abstract
Through its Green Continuum: Streets design guidelines (“The Continuum”), the City and County of Denver is implementing lower-impact low impact development on public projects in the right-of-way. The suite of green infrastructure tools outlined in the Continuum are intended to mitigate both stormwater runoff and urban heat island leading to cleaner, cooler, and more beautiful streets. The stormwater management approach is one of runoff reduction: infiltrating smaller, more frequent rain events carrying first flush pollutants into many shallow, pervious areas. This differs from the conventional, volume-based approach that has been in the city’s MS4 permit for decades. However, a “Runoff Reduction” pathway was added to Denver’s most recent MS4 permit that makes the control measures in the Continuum better suited for regulatory compliance. These control measures include shallow landscapes with no formal inflow/outflow, flow-through bioretention with little depression storage, and infiltration-only bioretention facilities. These proceedings will describe an Excel-based calculator that can aid in designing green infrastructure for regulatory compliance. The proceedings will include an overview of the runoff modeling and post-processing that underpins the calculator. This analysis includes development of regression equations that predict runoff with inputs available during design (e.g., soil classification, run-on ratio, geometry, etc.). The regression equations predicted runoff within 0.01 in. for a 0.6 in. design storm event in over 84% of 220,000 random green infrastructure configurations. While the calculator is developed for Denver’s regional climate and requirements, the proceedings offer a method for other municipalities looking to incorporate runoff reduction practices on right-of-way projects.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.036 |
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".