Optimal distribution of green and grey infrastructures coupled with real time control of the sewer for combined sewer overflows control as an adaptation measure to climate change
Bibliographic record
Abstract
Optimization of the spatial distribution of green infrastructures (GIs) was performed for a combined sewer system located in the Province of Quebec, Canada, using a simulation-optimization tool with the aim of reducing seasonal combined sewer overflows (CSOs). The performance of four CSOs control alternatives involving the individual and integrated implementation of GIs with storage tanks and real time control (RTC) of the sewer was evaluated for a nine-year simulation period of historical rainfall data and for 20%-increased rainfall data (representative of potential climate change impact). The integration of GIs with RTC of the sewer (with or without storage tanks) lowered the total CSO volume by 95% to 99% under historical rainfall data and by 93% to 96% under increased rainfall intensities when compared to the reference scenario. Adapting GI’s number and location for optimal CSO control rather than according to space availability criteria reduced CSO frequency but had only a slight impact on CSO volume reduction.
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".