Level of Service-Based Asset Management Framework for Water Supply Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Operators of North America’s potable water systems are facing numerous challenges in meeting the current needs and future expectations. Even though utility management experts started building customer-driven asset management systems to prioritize the water mains’ maintenance and replacement, the gap between the utility experts’ and end users’ perspectives still exists due to the lack of technical knowledge in terms of assessing the water quality. Therefore, this paper proposes a service-based asset management framework that evaluates the factors associated with the level of service (LOS) of the water supply networks and maps it to the physical condition. In this paper, the LOS for water supply networks is an indicator that measures the ability of a municipality to continuously supply the end users with adequate water quality to ensure fewer customer complaints and higher end-user satisfaction. The framework revolves around three phases: (1) data collection; (2) model implementation, which comprises LOS assessment and LOS and condition mapping models; and (3) results and analysis. To assess the LOS, a questionnaire was designed and analyzed using the best-worst method. Furthermore, an artificial neural network model was developed to map the relationship between the LOS and condition. Water quality, customer complaints, pressure, and continuity of water supply were used as mapping metrics between the LOS and condition. Toward the end, the framework was applied to the water distribution network of Montreal, Canada and it showed promising results in estimating the corresponding LOS from the condition. In addition, a cross-validation was carried out and the results displayed an 0.871 coefficient of determination (R2), which implies a strong existing relationship between the model inputs and outputs. This framework enables the utility experts to understand the customer perception of the service, optimize the budget allocation, and forecast the LOS based on the network condition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it