Key performance indicators for small and medium-sized urban water systems in a semi-arid region: a case study of Okanagan Valley, Canada
Bibliographic record
Abstract
Drinking water, wastewater, and stormwater are three components of an Urban Water System. Maintenance of these components requires evaluation of the existing performance of the water system. The evaluation becomes more significant in small and medium-sized water systems because these systems wrestle with various constraints, such as insufficient funds, inadequate infrastructure and water governance. In this study, Key Performance Indicators (KPIs) are identified for each UWS component considering six performance criteria. A questionnaire was distributed to water utilities across the Okanagan Valley. KPIs were identified by combining Delphi technique and Preference Ranking Organization Method for Enrichment Evaluation methods. Ninety-six KPIs were identified with 39, 30, and 27 KPIs for drinking water, wastewater, and stormwater. Based on the available literature, agriculture water use, low impact development implementation (LID), average annual life cycle investment, and swimming advisories are a few notable KPIs that are unique to the Valley.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".