Assessment of Urban Stormwater Systems for Improved Urban Habitat and Estuarine Ecosystem Management
Bibliographic record
Abstract
Water pollution caused by stormwater is a global issue as stormwater collects and transports various pollutants like chemicals, oils, and trash. Urbanization has led to increased untreated stormwater flowing through canals and ditches into natural water bodies, including rivers and lakes, which can lead to the growth of harmful algae. This study focuses on understanding the impact of coastal urban stormwater systems on the broader watershed and its community. The study area is the Halifax River, an estuarine lagoon along Florida's East Coast, and an impaired water body. Three urban outlets empty untreated stormwater into the river, but there has been a lack of water quality monitoring and assessment in the canal system. Precipitation data, water quality data, tide data, historical stormwater data, and management plans were used to provide information to aid in decision-making and implementing ordinances in urban stormwater management. In situ monitoring at the outfall sites and publicly available environmental data were analyzed. GIS maps of five adjacent municipalities within this stormwater drainage were compiled to highlight flood areas, census data, and critical infrastructure to analyze community impacts. The results indicate that Halifax River salinity generally has a decreasing trend over time while variation in turbidity has increased. Additionally, critical infrastructure (schools and hospitals) is affected most in Daytona Beach, with high minority percentages (49%) and a mean income of $41,200. Eight healthcare facilities would be affected by a category four hurricane storm surge along with 18 schools, 61% of businesses in Daytona Beach, and 91% of all businesses in South Daytona.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".