Selecting Sustainability Indicators for Small to Medium Sized Urban Water Systems Using Fuzzy‐ELECTRE
Bibliographic record
Abstract
Urban water systems (UWSs) are challenged by the sustainability perspective. Certain limitations of the sustainability of centralized UWSs and decentralized household level wastewater treatments can be overcome by managing UWSs at an intermediate scale, referred to as small to medium sized UWSs (SMUWSs). SMUWSs are different from large UWSs, mainly in terms of smaller infrastructure, data limitation, smaller service area, and institutional limitations. Moreover, sustainability assessment systems to evaluate the sustainability of an entire UWS are very limited and confined only to large UWSs. This research addressed the gap and has developed a set of 38 applied sustainability performance indicators (SPIs) by using fuzzy-Elimination and Choice Translating Reality (ELECTRE) I outranking method to assess the sustainability of SMUWSs. The developed set of SPIs can be applied to existing and new SMUWSs and also provides a flexibility to include additional SPIs in the future based on the same selection criteria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".