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Record W4362663514 · doi:10.2166/ws.2023.085

Mobile DMA testing for leakage assessment: perspectives from Ontario, Canada

2023· article· en· W4362663514 on OpenAlexaffabout
Bradley Jenks, Fabian Papa, Bryan Karney

Bibliographic record

VenueWater Science & Technology Water Supply · 2023
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of TorontoHydraTek (Canada)
Fundersnot available
KeywordsLeakage (economics)Software deploymentComputer scienceContext (archaeology)Reliability engineeringEnvironmental scienceEngineeringGeographyOperating system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.205
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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