Multi-Objective Decision Model for Urban Water Use: Planning for a Regional Water Reuse Ordinance
Bibliographic record
Abstract
Paul R. Anderson - Dept. of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology. \n \nWater use in much of the Great Lakes region is not consistent with sustainable growth concepts. For example, in the past the Chicago diversion from Lake Michigan has exceeded the decreed limit, and most of the water is used in applications that do not demand high quality water. Furthermore, the water and wastewater treatment processes dissipate a substantial amount of energy. Wastewater reuse in the Chicago metropolitan area could reduce the costs of municipal (drinking) water treatment, reduce the costs of wastewater treatment, reduce the amount of water diverted from Lake Michigan, and result in significant energy savings. Comprehensive planning for wastewater reuse is a multi-objective decision process that includes diverse issues with potential conflicts. There are, for example, treatment and distribution costs for municipal (drinking) water and for treated wastewater, and these costs depend on the distance between the water source and the reuse application. Furthermore, the flow of the Chicago River system affects transportation, habitat, water quality, and hydroelectric capacity, and these issues need to be considered. Finally, there are risk management and public perception/education issues that must be addressed whenever water reuse is promoted. We are developing a multi-objective decision model for urban water use, which can lay the foundation for a water reuse ordinance in the Chicago metropolitan area. In this project, we evaluated existing technological, economic, societal, and environmental incentives and barriers to wastewater reuse. Methods and information developed from this study is being presented through planning entities (the Chicago Metropolitan Agency for Planning in northeast Illinois) and technology transfer (the Illinois Waste Management and Research Center). We expect the methodology developed in this research will also have applications and bring benefits to other established urban centers that need to plan for sustainable water use, and all regional residents who make use of Great Lakes water resources. This project is a cooperative study involving the Illinois Institute of Technology, the Chicago Municipality Agency for Planning, and the Illinois Waste Management and Research Center (now Illinois Sustainable Technology Center).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".