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Record W2133299833 · doi:10.1061/40934(252)114

Leak Detection on Wastewater Forcemains and Siphons in North America Using the Sahara® Acoustic System

2007· article· en· W2133299833 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWastewaterPipeline transportBenchmarkingEngineeringMains electricityLeak detectionLeakElectricitySewage treatmentCivil engineeringEnvironmental scienceEnvironmental engineeringElectrical engineeringBusiness

Abstract

fetched live from OpenAlex

A series of pilot studies across North America have been held to demonstrate that the Sahara® leak detection system can be adapted to detect leaks in pressurized wastewater forcemains and siphons under typical North American operating conditions. Likewise, the Water Research Council (WRc) in the UK has demonstrated the applicability of the Sahara® leak detection system to similar environments in the UK. Sahara® wastewater is a new technology that allows utilities to assess the condition of critical wastewater forcemains and siphons, such as major non-redundant lines, waterway crossings, and lines through environmentally sensitive areas, while keeping the line in service; it is the first to allow full length inspections of in-service force mains. This paper discusses the need for inspections of wastewater forcemains and siphons, the Saraha® wastewater acoustic system, and the need for benchmarking of new technologies by third party organizations. The application of the Sahara system for the inspection of six wastewater pipelines in North America will also be discussed in detail.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.200

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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