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Record W1989627346 · doi:10.1061/40934(252)17

Robotic RFEC/TC Inspection of Transmission Mains with Reducers: Practical Aspects

2007· article· en· W1989627346 on OpenAlexaff
Allison Psutka, Xiangjie Kong, Lily Gao, Dave Caughlin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsResearch Canada
Fundersnot available
KeywordsMast (botany)TransformerMains electricityComputer scienceElectric power transmissionLine (geometry)Nondestructive testingEddy currentMarine engineeringEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

In 2005, a non-destructive testing evaluation was conducted on a 36-inch Prestressed Concreter Pipe (PCP) line for a large water utility in Illinois using the patented Remote Field Eddy Current / Transformer (RFEC/TC) Technology. RFEC/TC determines the number and location of wire breaks in individual PCP pipe. Due to the small diameter of this line, both man-operated bicycle-type tool and the remote-operated tethered system were used for the inspection. Data collected from the two systems were both of good quality and comparable to each other. The utility recognizes the value of the RFEC/TC inspection and plans to conduct more inspections in other PCP sections. When possible, the remote-operated tethered system, known as PipeCrawler, will be utilized because of safety considerations and because PipeCrawler is capable of inspecting pipes in a water-filled condition. In November 2006, the same water utility used the RFEC/TC technology to inspect approximately 4 miles of another 36-inch line. This inspection was challenging because it contained seven 36-inch by 24-inch reducers. Consequently, it was not possible to use the man-operated bicycle tool. In addition, it would not have been possible to use the PipeCrawler tool in its normal configuration, because of its fixed length mast. To overcome this challenge, a controllable and extendable mast was designed to fit on the PipeCrawler to facilitate the RFEC/TC inspection of PCP mains with reducers. In this paper, we will discuss the practical aspects of this.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2007
Admission routes1
Has abstractyes

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