The Urban Drainage Program of Canada’s Great Lakes 2000 Cleanup Fund
Bibliographic record
Abstract
Abstract Since 1990, Canada’s Great Lakes 2000 Cleanup Fund, which is administered by Environment Canada, has been supporting the development and implementation of cleanup technologies to control municipal pollution sources, to clean up contaminated sediments, and to rehabilitate fish and wildlife habitats. These efforts are focused on Canada’s 16 Great Lakes Areas of Concern (AOCs) identified by the International Joint Commission for priority cleanup action and restoration of beneficial uses. Remedial Action Plans (RAPs) developed by federal/provincial teams and the public provide the strategy for restoring the beneficial uses of the AOCs. Impairments in beneficial uses in the AOCs have been, in part, caused by discharges from combined sewer overflows (CSOs), Stormwater and sewage treatment plants (STPs). To assist municipalities in addressing the problems posed by urban drainage (CSOs and Stormwater), the Cleanup Fund’s Urban Drainage Program has been supporting the development and demonstration of innovative, cost-effective technologies and approaches. These projects include high-rate treatment of CSOs, real-time control of CSOs, performance assessment of Stormwater treatment technologies, pollution prevention and control plans, and development of Stormwater management planning tools for urban areas. These projects are carried out in collaboration with the Ontario Ministry of the Environment, municipalities, professional groups, universities and conservation authorities and other Environment Canada’s facilities (National Water Research Institute and Wastewater Technology Centre). The Urban Drainage Program has been instrumental in advancing the state of the art in CSO and Stormwater management in Ontario. Projects supported under the program have quantified pollutant loadings from municipal wastewater sources in several Ontario Areas of Concern, provided hard data on the performance of best management practices for Stormwater treatment, identified and evaluated new cost-effective technologies for CSO reduction and Stormwater treatment, and developed strategies and decision-making tools for Stormwater management The work done through the Urban Drainage Program is making it possible for Great Lakes communities to achieve important environmental objectives at significantly lower cost As a result, the communities should be able to achieve many of these objectives much earlier than they would have if their choices had been limited to more conventional and capital-intensive solutions. Although the program has focused on the needs of Areas of Concern in the Great Lakes basin, the lessons learned there can easily be applied to communities in other parts of the country and around the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".