Evaluation of nutrient retention and sediment deposition in two urban stormwater retention ponds
Bibliographic record
Abstract
Stormwater ponds have been implemented in many municipalities to control urban runoff and retain pollutants, such as nutrients and suspended solids. Two stormwater ponds in Toronto, Ontario were evaluated for their ability to retain nutrients and suspended solids and were also used to investigate mechanisms by which stormwater ponds remove nutrient pollutants, including the importance of deposition and internal loading. Over the entire study period, Hydro Pond East (HEP) retained 1415 mg of total suspended solids (TSS) and MAT retained 1127 mg of TSS. Both Hydro East Pond (HEP) and Mattamy Rouge (MAT) were net exporters of phosphorus (P) over the entire season, with 6.35 mol or 0.20 kg and 53.9 mol or 1.67 kg exported, respectively. HEP had net retention of 2672 mol or 37.4 kg of nitrogen (N) but MAT exported 264 mol or 3.7 kg of nitrogen over the entire study. This study has demonstrated that stormwater ponds have the ability to provide retention of nutrients and TSS, but their function may be enhanced as they may become exporters. However, the amount of nutrients exported was extremely low and may have been driven by the anomalously dry 2016 year in Toronto. Further research should be done on these same ponds to observe how they may perform under an anomalously wet year (e.g. 2017). There is a need for a future model to synthesize the data from literature on stormwater ponds to better understand their function to better help local water managers determine if these ponds are needed and how they may need to enhance their function.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".