Hierarchical Optimization of Integrated Water Reuse Schemes
Bibliographic record
Abstract
With up to 40% of the world's population in over 80 countries and regions experiencing water stress, water reuse has emerged as a genuine and reliable alternative that can be used to supplement, and in some cases substitute, traditional water sources.The already large number of water reuse schemes, existing primarily in areas of fresh water shortage, is expected to increase in the future due to several factors.In industrialized countries, the main drivers are the lack of dependable water supplies and wastewater disposal sinks, while the need to provide economically feasible new water supplies and protect existing water sources from pollution are the key factors influencing water reuse in the developing world (Asano 1999).An important factor that needs to be considered in the context of planning of future water reclamation undertakings is the scale of the projects.The water reclamation projects are driven by demand and lack of alternative sources, which lead to projects being considered, or even made feasible, by securing one or several large customers, which offers greater demand security than securing smaller customers.As the demand for reclaimed water rises in the future, the number of potential end-users will also increase, leading to larger and more complex projects requiring adequate tools for their development.In the city of Chicago, for example, which currently reuses only 2% of effluents discharged from its seven wastewater treatment plants (WWTPs), a study of future water reuse opportunities identified over 800 potential users (Meng 2005).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".