A Framework for Evaluating the Effects of Green Infrastructure in Mitigating Pollutant Transferal and Flood Events in Sunnyside, Houston, TX
Bibliographic record
Abstract
There is a growing and critical need to develop solutions for communities that are at particular risk of the impacts of the nexus of hazardous substances and natural disasters. In urban areas at high risk for flooding and lacking proper land-use controls, communities are vulnerable to environmental contamination from industrial land uses during flood events. This research uniquely applied a series of landscape pzerformance models to evaluate such associations including (1) the Green Values National Stormwater Calculator, (2) the Value of Green Infrastructure Tool, and (3) the Long-Term Hydrologic Impact Assessment Model. This paper presents a framework for combining landscape performance models, which are often only individually applied, to evaluate green infrastructure impacts on flood mitigation and pollutant transfer during flooding events using the Sunnyside neighborhood in Houston, Texas, USA, as a case site. The results showed that the plan reduced the risk of flooding, decreased stormwater runoff contaminants, and provided a possible direction to protect vulnerable communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".