Critical issues for stormwater ponds: learning from a decade of research
Bibliographic record
Abstract
The Queen's University/National Water Research Institute Stormwater Quality Enhancement Group has been actively researching stormwater ponds for the past decade, using a fully instrumented on-line system in Kingston, Ontario, Canada as a representative field installation of this group of stormwater best management practices, along with comprehensive surveys of other facilities as well. From this body of research, the Group has concluded that there are a number of identifiable factors, termed critical issues, which will significantly influence the success, failure and sustainability of these BMPs. Such factors will be important to a very diverse group of stakeholders in stormwater management, including designers, owners/operators, regulatory authorities and the general public. These factors can be grouped within the categories of initial design, operation and maintenance, performance and adaptive design. From this work, it is concluded that the so-called first generation quantity-control ponds may be outdated today, compared with the modern focus on quantity and quality issues in the second generation systems; nonetheless, without consideration of these critical issues and flexible design practices which can account for emerging or future issues, the current systems also run the risk of becoming outdated before the end of their design lives.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".