Urban Stormwater Phosphorus Export Control: Comparing Traditional and Low-impact Development Best Management Practices
Bibliographic record
Abstract
Data from the International Stormwater Best Management Practices (BMP) Database were used to compare the phosphorus (P) control performance of six categories of stormwater BMPs representing traditional systems (stormwater pond, wetland basin, and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale, and grass strip). Machine learning (ML) models were trained to predict the reduction or enrichment factors of surface runoff concentrations and loadings of total P (TP) and soluble reactive P (SRP) for the different categories of BMP systems. Relative to traditional BMPs, LIDs generally enriched TP and SRP concentrations in stormwater surface outflow and yielded poorer P runoff load control. The SRP concentration reduction and enrichment factors of LIDs also tended to be more sensitive to variations in climate and watershed characteristics. That is, LIDs were more likely to enrich surface runoff SRP concentrations in drier climates, when inflow SRP concentrations were low, and for watersheds exhibiting high impervious land cover. Overall, our results imply that stormwater BMPs do not universally attenuate urban P export and that preferentially implementing LIDs over traditional BMPs may increase TP and SRP export to receiving freshwater bodies, hence magnifying eutrophication risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".