Repurposed Wetlands - A Case Study of a Reconstructed Urban Stormwater System along East Central Florida’s Halifax River
Bibliographic record
Abstract
Wetlands play a critical role in functioning watershed systems. They act as natural sinks for absorbing excess water, mitigating flood risk while also removing and sequestering surplus nutrients, pollutants, and sediments from stormwater runoff before it enters other water bodies. In highly urbanized areas, impervious surfaces restrict runoff infiltration, exacerbating flooding and pollution impacts. The Halifax River, an urbanized estuarine lagoon system located in East Central Florida and an important part of the broader Halifax watershed, is encompassed by large portions of impervious surfaces in the adjacent cities. In this case study, an existing detention pond connected to the Halifax River outfall canal system was retrofitted with a stormwater treatment system to help improve filtration of pollutants and excess nutrients. This project details the planning, construction, and in-situ water quality data collection and monitoring, documenting the before, during, and after impacts of the treatment wetland’s construction. The initial monitoring began in Winter 2017 and concluded post-construction monitoring in Summer 2019. Remote sensing images from Dec. 2017, Nov. 2019, Nov. 2021, and Jan. 2023 were downloaded from the USGS Earth Explorer and analysed with vegetation indices (NDVI) to evaluate the wetland’s long-term productivity trends and current conditions. Additionally, single and multivariate statistical analyses were conducted with the water quality data, such as salinity, TKN, and NO3, and evaluated across the project’s stages (pre-, during, and post-restoration). The remote sensing analysis indicates development and maturation of the wetland vegetation, while multivariate analysis shows significant differences in water quality variables post-restoration compared to those collected at the pre- and during stages. Treatment wetlands can better support urbanized areas, particularly when retrofitting the existing system, reducing hazard and pollution impacts while creating hotspots for biodiversity and increasing watershed functionality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".