Ecosystem Functions in Urban Stormwater Management Ponds: A Scoping Review
Bibliographic record
Abstract
Stormwater management ponds (SWMPs) are an important tool for sustainable urban stormwater management, controlling the quantity and quality of stormwater runoff in cities. Beyond their engineering purpose, SWMPs may hold ecological value that is often overlooked. This is especially the case for the array of geochemical, physical, and biological processes (i.e., ecosystem functions) in SWMPs. Here, we performed a scoping review of ecosystem function in SWMPs to summarize current knowledge and identify research needs. We searched peer-reviewed papers using the Web of Science database. Papers that did not report specifically on SWMPs, did not discuss ecosystem function, or were solely based on ecotoxicological tests were excluded from further assessment. For the remaining papers, information on year of publication, scope, and key findings was extracted. We found that a total of 55 papers on ecosystem function in SWMPs have been published since 1996. Our review identified important areas for advancing knowledge about nutrient dynamics, contaminants processing, sedimentation, temperature, habitat provisioning, and biodiversity in SWMPs. Overall, we identified a need to further understand how factors related to pond design and landscape and management practices influence ecosystem function. There is also a need to understand the effect of climate change on ecosystem function and to examine the interactions between ecosystem function and humans. Such information will not only provide opportunities for researchers to better understand ecological value, but also facilitate more effective sustainable management of SWMPs.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".