Performance Assessment Method for Small- and Medium-Sized Urban Water Systems: Development and Implementation
Bibliographic record
Abstract
Performance assessment of Small and Medium-Sized Water Systems (SMWSs) is important for operational, tactical, and strategic decision-making. In this study, a performance assessment method has been developed and applied to five drinking water, three wastewater, and two stormwater utilities using 39, 30 and 27 Key Performance Indicators (KPIs) in a semi-arid region. The KPIs were aggregated to determine a performance index using a Technique for Order of Preference by Similarity to Ideal Solution method. K-nearest neighbors and penalty methods were used to estimate missing KPIs data. The results indicated that only two drinking water utilities and one wastewater utility had been rated as ‘high’ performance. None of the utilities in stormwater performance was rated as ‘high’. The developed method can assist decision-makers in evaluating SMWSs performance holistically, build operational management strategies, and identify necessary interventions in overcoming water systems challenges across each urban water system component.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".