Global predictive real-time control of Quebec Urban Community's westerly sewer network
Bibliographic record
Abstract
Quebec Urban Community (QUC) has selected Global Predictive Real-Time Control (GP-RTC) as the most efficient approach to achieve environmental objectives defined by the Ministry of Environment. QUC wants to reduce combined sewer overflows (CSOs) frequency to the St Lawrence river to two events per summer period in order to reclaim the use of Jacques-Cartier Beach for recreational activities and sports of primary contact. QUC's control scheme is based on the Certainty Equivalent Control Open Loop Feedback (CEOLF) strategy which permits one to introduce, at each control period, updated measurements and meteorological predictions. A non-linear programming package is used to find the flow set points that minimise a multi-objective (cost) function, subjected to linear equality and inequality constraints representing the physical and operational constraints on the sewer network. Implementation of GP-RTC on QUC's westerly network was performed in the summer of 1999 and was operational by mid-August. Reductions in overflow volumes with GP-RTC compared to static control are attributed to the optimal use of two existing tunnels as retention facilities as well as the maximal use of the wastewater treatment plant (WWTP) capacity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".