A comparison of phosphorus retention in conventional and naturalized stormwater ponds in Winnipeg, Manitoba
Bibliographic record
Abstract
Urban stormwater ponds are utilized to temporarily retain stormwater runoff and mitigate flooding associated with increased impervious surfaces. Stormwater runoff can impair water quality by transferring nutrients, such as phosphorus and other pollutants, to receiving waters. Phosphorus is an important stormwater pollutant due to its role in promoting eutrophication and algal blooms. This study evaluated total phosphorus (TP) concentrations in two types of stormwater ponds: conventional stormwater ponds (CSPs) and naturalized stormwater ponds (NSPs). Both pond types are designed to retain peak stormwater flows to alleviate flooding, but NSPs are designed to incorporate emergent wetland vegetation for numerous benefits, including nutrient removal. The objective of the study was to compare TP concentrations in eight CSPs and eight NSPs. Water quality samples were collected approximately biweekly from June to November in Winnipeg, Manitoba, Canada. Averaged across pond type, TP concentrations in CSPs ranged from 0.2 to 0.8 mg/L and were three to eight times greater than in NSPs, where TP concentrations were below 0.2 mg/L. During summer, TP concentrations in CSPs reached a maximum of 0.9 to 3.3 mg/L. The findings demonstrate that incorporating NSPs into urban development is a better approach than CSPs for reducing phosphorus release into downstream environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".