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Record W2300215658 · doi:10.14796/jwmm.r235-23

A Simulation Study on Using Inverse Transient Analysis for Leak Detection in Water Distribution Networks

2009· article· en· W2300215658 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Water Management Modeling · 2009
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsNational Research Council CanadaUniversity of Toronto
FundersUniversity of Toronto
KeywordsLeak detectionTransient (computer programming)LeakEnvironmental scienceTransient analysisComputer scienceDistribution (mathematics)EngineeringCivil engineeringEnvironmental engineeringTransient responseMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Inverse transient analysis, developed by several researchers in recent years as a promising and low-cost leak detection technique, has been successfully demonstrated under laboratory settings. However, the feasibility and technical limitations of this technique under actual field conditions has yet to be definitely established. This chapter reports on the initial simulation phase of a study that aims to assess the applicability and effectiveness of inverse transient analysis in the detection of leaks in real water distribution systems. Simulation was conducted and field tests are planned for a selected area of the City of Regina's municipal water distribution network. Scenarios of various transient severity and leak sizes were simulated. Insight gained from the simulation provided a preliminary assessment of the effectiveness and feasibility of using the inverse transient analysis method for leak detection.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.534
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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