Bibliographic record
Abstract
Abstract A water utility consists of different key components (to operate one or more water supply systems in its geographical jurisdiction), including water resources and environmental, physical assets, operations, public health, services, personnel, and finance. Performance of these components can be assessed using suitable performance indicators (PIs). Indicators should be cautiously selected based on their relevance, measurability, and comparability for both the inter‐utility benchmarking and Intra‐utility performance assessment. Inter‐utility benchmarking process may generate long documentation presenting the comparisons of several indicators. Conversely, performance indices are generated by aggregating the PIs at the component level, which are more convenient to top‐level utility's management, policy makers, and the general public. Operations managers are more engrossed in the underlying processes. Intra‐utility performance assessment informs about the performance of subcomponents for each water supply system operating in a utility's dominion. Knowledge shared in this article reveals that effective use of PIs can contribute to the overall sustainability of our water supply systems. More recent applications, (i) multilevel performance assessment, (ii) risk‐based benchmarking, and (iii) continuous performance improvement, are also discussed.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".