Mobile DMA testing for leakage assessment: perspectives from Ontario, Canada
Bibliographic record
Abstract
Abstract The formation of discrete regions in a water distribution system, referred to as district metered areas (DMAs), can be a pragmatic approach to diagnose both system and leakage characteristics. Their application, however, has historically been limited in the North American context owing in part to their costly implementation and operational challenges. Both to overcome these barriers and to demonstrate the benefits of DMAs, a leakage testing programme was undertaken in Ontario, Canada. Novelty arises from the development and deployment of a mobile testing unit specifically designed to collect minimum night flow (MNF) and pressure data into temporarily configured DMAs. Moreover, activation of a pressure reducing valve facilitated the direct testing of pressure modulation on leakage reduction. The mobile unit was deployed in 22 DMAs across eight water systems with results indicating a clear relationship between MNFs and system characteristics for well-performing DMAs. MNF benchmarks were quantified to enable an evidence-based assessment of leakage performance at the DMA level in typical Canadian water systems. This project established the proof-of-concept of the mobile unit for providing both accurate and reliable measurements of key leakage performance characteristics and for predicting leakage reduction following system interventions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".