EVALUATION OF PERFORMANCE INDICATORS OF SELECTED WATER COMPANIES IN VIETNAM
Bibliographic record
Abstract
Performance indicators of water supply company can provide important information of its service quality and business efficiency, and be intelligent basis for decision making process. The authors have analyzed key performance indicators of 19 selected municipal water supply systems in Vietnam, including operation and design capacities, treated water quality, unit investment cost, water tariff, non-revenue water (NRW) ratio, and energy consumption rate. The average NRW of the 19 systems was 12.6% which was lower than country-wide value of 21%. The energy consumption rate of selected systems was ranging from 0.16 to 0.5KWh/m3, in average 0.3KWh/m3, which was also lower than country average of 0.35KWh/m3, whereas the rate of energy consumption in municipal water systems in China, USA, Australia, Chile, Canada was ranging from 0.1 to 1.33KWh/m3, depending on ground elevation, transfer distance, influent water quality, and applied technologies for water treatment and transportation. The selected water systems have applied improved treatment technologies such as mechanized coagulation-flocculation, lamella settling tank, dual media sand filter, combined contact clarifier with lamella plates, etc. The average treated water turbidity was ≤0.5NTU. The domestic water tariff of the selected systems was within the country range, from USD0.2 to 0.4/m3. Further, the authors have indicated correlation between selected performance indicators, such as energy consumption rate and non-revenue water ratio. The analytical results shown performance indicators of top water companies in Vietnam were in fairly good position compared to others, but improvements were still needed. Reduction of NRW ratio and keeping it at a low value are other challenges requiring water utility efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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".