Evaluation of Low Impact Development Stormwater Technologies and Water Reuse Options for the Lake Simcoe Regions
Bibliographic record
Abstract
In support of the efforts of the Lake Simcoe Region Conservation Authority (LSRCA) to meet its Vision for the Restoration and Protection of Lake Simcoe and its Watershed, Ryerson University conducted a study to evaluate the suitability and effect of implementation of non-conventional wastewater and stormwater control technologies, including low impact development technologies (LID) and water reuse options, within the pre-defined uncontrolled study area where conventional stormwater management practices were not feasible. The aims of the study project were to: identify opportunities for implementation of these technologies, quantify at a planning level the benefits that could be provided in terms of reduced nutrient loadings to Lake Simcoe, and ultimately provide guidance to municipalities within the watershed. Phase I compiled all the existing data and information, summarized the previous studies and projects that had been carried out to date deemed relevant to the project, and carried out a geographic information system analysis (GIS) of the opportunities for implementation of LID (based on suitability criteria such as land use and physical site requirements). Phase II carried out a more detailed study of the usage of LID identified in Phase 1 as being potentially suitable, evaluated the best combinations of LID (and their placement), quantified preliminary costs of their implementation and pollution reduction benefits such as annual nutrient loading reduction, and examined the opportunities for reclamation of wastewater and stormwater in the study area. Additionally, the effects of future development and climatic changes on the overall efficiency of promising solutions were evaluated. The modeling of the pollution reduction benefits was based on the development of hydrologic unit response functions (URF) for different land uses and LID combinations and the aggregation of these URF over the study area using GIS. The study findings indicate that the implementation of the feasible LID such as bioretention cell, rainwater harvesting, greenroof, and downspout disconnection could potentially reduce the nutrient loading from the uncontrolled study area by about 10 to 20%.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".