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Record W2074296298 · doi:10.1115/ipc2012-90372

CEPA Development of a Metal Loss ILI Acceptance Procedure

2012· article· en· W2074296298 on OpenAlexaff
Guy Desjardins, Ryan Sporns, Reena Sahney, Joe Yip

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsDesjardins
Fundersnot available
KeywordsComputer scienceVendorPipeline (software)Process (computing)Reliability engineeringAcceptance testingFormal verificationSoftware engineeringEngineeringOperating systemAlgorithm

Abstract

fetched live from OpenAlex

This paper presents guidelines to enable a pipeline operator to assess whether the results of a metal loss in-line inspection (ILI) should be deemed acceptable. Acceptance depends on passing two checks, Verification and Validation. Verification is the process which ensures that all planning, preparation, execution, and analysis of the inspection were conducted according to ILI vendor processes to meet specification and prescribed industry standards. It assumes that a successful run is a consequence of good planning, preparation, execution and analysis. Validation is the process by which the results of an ILI run are compared to independent measurements. The Validation process depends on the results of the ILI and available information. Passing both the Verification and Validation checks is required to accept of the ILI run.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.399

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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 teacher head, 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

Citations0
Published2012
Admission routes1
Has abstractyes

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