Seasonal hydrological and water quality performance of individual and in-series stormwater infrastructures as treatment trains in cold climate
Bibliographic record
Abstract
Abstract The performance of stormwater treatment trains and of their individual green infrastructures was evaluated near Montreal, Canada. Three treatment trains were studied: Train 1 – five bioretention cells in series with a wet retention pond; Train 2 – an infiltration trench in series with a dry detention pond; and Train 3 – Train 2 in series with a wet retention pond. A total of 47 rain events were monitored to quantity the hydrological performance, while water quality samples were taken during 24 rainfall events. During the summer, the bioretention cells led to a reduction in runoff volumes varying from 8 to 100%. Overall, the three studied treatment trains and all of the individual infrastructures, except for the dry pond, provided reductions in the mean concentrations of total suspended solids, chemical oxygen demand, total nitrogen and total phosphorous. Results also showed that the use of a train of stormwater infrastructures can be more effective to reach Quebec's legislated targets than single infrastructures, but only if the infrastructures are sequenced properly. Indeed, the addition of a dry basin at the end of Train 2 affected negatively the removal efficiency of the four studied contaminants.
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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 | 0.001 | 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".