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Record W2042353798 · doi:10.1115/ipc2014-33294

Investigation and Assessment of Low-Frequency ERW Seam Imperfections by EMAT and CMFL ILI

2014· article· en· W2042353798 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
TopicNon-Destructive Testing Techniques
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsElectromagnetic acoustic transducerEngineeringAcoustic emissionAcousticsElectrical engineeringUltrasonic testingUltrasonic sensor

Abstract

fetched live from OpenAlex

The occurrence of low-frequency Electric Resistance Welded (LF-ERW) or Electric Flash Welded (EFW) line pipe imperfections has been the root cause of many integrity management initiatives to minimize and mitigate the risk of pipeline failure across the oil & gas pipeline industry. Since their first appearance in the 1920s, defects in or near the LF-ERW and EFW seam repeatedly lead to either hydrostatic test or in-service failures. Where in the past In-Line Inspection (ILI) technologies might have experienced limitations in addressing vintage ERW line pipe defects, modern smart ILI technologies show enhanced capabilities. High resolution Electro-Magnetic Acoustic Transducer (EMAT) and Circumferential Magnetic Flux Leakage (CMFL) ILI technologies have advanced in the recent years enabling more challenging inspections. This paper summarizes the inspection results of 22″ ERW line pipe defects detected and reported by EMAT and CMFL. Correlation of ILI and manual NDE data enables evaluation of current ILI capabilities and improvement of current defect assessment methods.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.363

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 head0.009
GPT teacher head0.241
Teacher spread0.231 · 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
Published2014
Admission routes1
Has abstractyes

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