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Record W2006623079 · doi:10.1061/9780784413692.017

What In-Line Technologies Work Best for Condition Assessment of Pipelines and Why

2014· article· en· W2006623079 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

VenuePipelines 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPipeline transportMagnetic flux leakagePipeline (software)Leakage (economics)EngineeringLine (geometry)Oil fieldComputer scienceAsset (computer security)Petroleum engineeringMarine engineeringMechanical engineeringComputer security

Abstract

fetched live from OpenAlex

Direct condition assessment (DCA) of pipelines in water, waste water, oil, or gas service is growing in popularity due to the fact that asset managers need to know the condition of their pipelines, in detail, to maintain them and extend their life. Knowing which DCA tool is best for a given type of pipeline is an important step in selecting the best technology. This paper discusses the attributes of in-line inspection (ILI) tools that utilize remote field technology (RFT), ultrasonic (UT), electromagnetic acoustic technology (EMAT), and magnetic flux leakage (MFL) tools and discusses the strengths and limitations of each.

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 score0.599
Threshold uncertainty score0.492

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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