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Record W2039302633 · doi:10.1115/ipc2010-31668

Understanding Magnetic Flux Leakage Signals From Gouges

2010· article· en· W2039302633 on OpenAlex
L. Clapham, Vijay Babbar, Jian Dien Chen, Chris Alexander

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

Venue2010 8th International Pipeline Conference, Volume 1 · 2010
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsMagnetic flux leakageIndentationMaterials scienceGeologyComposite materialMagnetEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The Magnetic Flux Leakage (MFL) technique is sensitive both to pipe wall geometry and pipe wall strain, therefore MFL inspection tools have the potential to locate and characterize mechanical damage in pipelines. The present work is the first stage of a study focused on developing an understanding of how MFL signals arise from pipeline gouges. A defect set of 10 gouges were introduced into sections of 12″diameter, 5m long, end capped and pressurized X60 grade pipe sections. The gouging tool displacement ranged (before tool removal) between 2.5 to 12.5mm. Gouges were approximately 50mm in length. The shallowest indentation created only a very slight scratch on the pipe surface, the deepest created a very significant gouge. All gouges were axially oriented. Experimental MFL measurements were made on the external pipe wall surface (pressurized) as well as the internal surface (unpressurized). The early results of the experimental MFL studies, and a hypothesis for the origin of the MFLaxial signal “dipole” are discussed in this paper.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.058
GPT teacher head0.255
Teacher spread0.198 · 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