Drinking water management and governance in small drinking water systems : integrating continuous performance improvement and risk-based benchmarking
Bibliographic record
Abstract
Drinking water suppliers face challenges associated with changing populations, evolving economies, aging infrastructure, and shifting consumer demands. In small drinking water systems (SDWSs), these challenges are amplified by the pressure created from financial shortfalls and limited human resources. SDWSs are prone to higher rates of drinking water quality failure, are more vulnerable to spatiotemporal variability in water quality, and may be more susceptible to waterborne disease outbreaks than larger systems. Despite these challenges, SDWSs are overlooked in traditional academic and industrial studies, which often place a focus on larger, more complex drinking water supply systems (DWSSs) and the exploration, development, and implementation of new treatment technologies. Given the current state of SDWSs, the main objectives identified for this research were to incentivize continuous performance, improve data resolution and water quality assessment practices for decision-making, and propose an improved drinking water quality management approach for SDWSs. This was accomplished in four distinct steps. The first step was to review the current state of practice of quality management systems and drinking water management systems and approaches in different parts of the world and within Canada to identify management gaps and potential areas for improvement. The second step was to explore the concept of continuous performance improvement and incentivize implementation through functional performance benchmarking. The third step was to improve on current drinking water quality assessment and benchmarking practices by implementing risk through quantifying degrees of compliance/non-compliance and spatial (i.e. location in the distribution system) and temporal (i.e. seasonal) variability through fuzzy rule-based modeling. The fourth and final step was to propose an improved drinking water management framework that fits within the bounds of Canada’s current decentralized governance system. The results of this research have the potential to be used by drinking water utility managers, operators, and planners to improve drinking water quality management in SDWSs at the federal, provincial/territorial, and municipal levels and improve on the current drinking water quality assessment and decision-making processes in place.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".