Study on bioretention for stormwater management in cold climate, part II: water quality
Bibliographic record
Abstract
Abstract Typical continental, cold climate conditions were applied to four large, laboratory columns to simulate temperatures ranging from −20 to +20 °C over summer, winter, and spring runoff. The goal of this research was to assess the seasonal effectiveness of bioretention application in cold climate regions that are currently experiencing the impacts of climate change. 1.6 years’ worth of equivalent Edmonton precipitation volume was applied over a 10-month period to evaluate the water quality improvement through two soil types (i.e., loam and sandy loam) with and without amendments intended to enhance nutrient removal. During summer, excellent removal of total suspended solids (TSS), phosphate, and ammonium (i.e., ≥90% average concentration reduction) was observed in both the loam and sandy loam columns without the nutrient removal amendments. The columns containing the amendments also reduced TSS, ammonium, and phosphate, but not as effective as the non-amended columns for TSS and ammonium. During winter and spring runoff, if infiltration occurs, physical removal of TSS, phosphate, and ammonium was still achieved, only days after the complete freezing (and subsequent thawing) of bioretention media at −20 °C. After an initial maturation and leaching period, nitrate was well removed (≥65.2% average concentration reduction) via denitrification in the columns with the nutrient removal amendments and submerged zone.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".