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Record W1967039969 · doi:10.1115/ipc2012-90251

Comparison of In-Line Inspection Service Provider Magnetic Flux Leakage (MFL) Technology and Analytical Performance Based on Multiple Runs on Pipeline Segments

2012· article· en· W1967039969 on OpenAlexaff
Guy Desjardins, Randy Nickle, Darren Skibinsky, Joe Yip

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsPetroleum Technology Alliance CanadaDesjardins
Fundersnot available
KeywordsVendorMagnetic flux leakagePipeline (software)Leakage (economics)AlliancePipeline transportPrioritizationMagnetic fluxComputer scienceEngineeringMechanical engineeringMagnetic fieldBusinessPhysics

Abstract

fetched live from OpenAlex

This paper presents the results of a comparison between three In-Line Inspection (ILI) vendor’s high resolution magnetic flux leakage (MFL) inspections. Between 2009 and 2011, Alliance Pipeline Ltd. (Alliance) commissioned the inspection of a number of pipeline segments, where each vendor inspected all segments. These inspections have enabled Alliance to conduct a round-robin comparison of the performance and capabilities of each of the vendor’s abilities to detect and size metal-loss anomalies.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations7
Published2012
Admission routes1
Has abstractyes

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