Green infrastructure practices: alternative systems for stormwater management in New Brunswick, New Jersey
Bibliographic record
Abstract
As global urbanization continues to expand cities, the systems that operate in the background to allow cities to function are being stressed. Water management systems in particular are a growing concern in the United States. Water quality and quantity are becoming increasingly significant issues as global climate change is producing unprecedented drought and flood periods across the world. The nation’s traditional combined sewer overflow (CSO) systems for storm and wastewater are aging, outdated, and overburdened due to rapid urbanization and vast impervious surface coverage. The alternative to traditional “hard, gray” infrastructure systems engineered by humans are known as “soft” or “green” infrastructure, which instead integrates plants and landscapes into built environments for a variety of structural, aesthetic, and community services and benefits. Studies continue to prove that using green infrastructure systems to help manage stormwater runoff is a viable, long-term, cost-effective solution for communities that suffer from frequent flood, rainfall, or combined sewer overflow events. The following research focuses on New Brunswick, New Jersey, a colonial-era city that experiences frequent flooding as a result of the nearby Raritan River. Target sites for green infrastructure intervention are determined through geographic information system (GIS) analyses, historical data, and direct observation of water-related problems. A variety of best management practices (BMPs) are proposed for each target site, followed by conceptual designs and estimated evaluations of the proposed best management practice’s impact. This document will serve as a book and model for similar cities to help address their stormwater management issues in a sustainable, efficient manner.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".