A Planning Framework For Low Impact Development (LID) In Stormwater Management - An Ontario Perspective
Bibliographic record
Abstract
Effective management of stormwater is critical to the continued health of the environment. Progression of stormwater management techniques has evolved to include wider, sustainable objectives, particularly the development of Low Impact Development (LID) methods. Despite the recognition that the application of LID practices is a viable approach to older forms of stormwater management, there exist various challenges and barriers to widespread support. In particular, absent is a methodology to plan for LID practices on a large-scale that encompasses not only technical criteria, but economical, and social aspects as well. To address this need, the objective of this study proposes a framework for LID planning on a watershed level. The LID planning Framework is comprised of four main components evaluated in a sequential process to support the development of effective management strategies. Specifically, hydrological performance evaluation of LID technologies throughout a watershed; cost-effectiveness analysis; and stakeholders’ opinions and acceptance levels of these technologies, are used as input to the final decision-making component. The LID Planning Framework is developed in an Ontario context with a particular focus on the Lake Simcoe Watershed. This study will promote an integrated approach to LID planning, which can be used support the uptake of LID principles and encourage more sustainable methods in stormwater management as a whole.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".