Performance indicators for small- and medium-sized water supply systems: a review
Bibliographic record
Abstract
Water supply systems (WSSs) are one of the most important and expensive core public infrastructures. The primary objective of a water supply utility is to have this valuable asset operate at its maximum possible efficiency with minimum cost throughout its design period. To achieve this objective, the first step is to evaluate the existing efficiency of all the components of the WSS using suitable performance indicators (PIs). Various agencies and organizations worldwide have developed detailed performance evaluation frameworks including several indicators to comprehensively cover all the aspects (e.g., physical asset, staffing, operational, customer satisfaction, economical) of the WSSs. Most of these frameworks and indicators have been developed for large-sized WSSs. Small- and medium-sized water supply systems (SM-WSSs) have specific performance-related issues, ranging from difficulties in collecting the data required to use the available systems of PIs to lack of skilled personnel and financial resources for efficient operations. A comprehensive review of the literature has been carried out to assess the suitability of reported performance evaluation systems for SM-WSSs in terms of their simplicity (easy and simple data requirements) and comprehensiveness (i.e., all the components of a WSS). This review also evaluates the individual PI with respect to its understandability, measurability, and comparability (i.e., within and across utility comparisons). On the basis of this detailed review, a conceptual performance evaluation system for SM-WSSs, consisting of a list of PIs grouped into their respective categories, has been proposed. The proposed system provides a stepwise approach, starting the performance evaluation process with the most significant and easy to measure PIs for small-sized WSSs and moving to a relatively complex set of indicators for SM-WSS depending on the availability of resources and specific operating conditions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".