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Record W2549301392 · doi:10.1115/ipc2016-64701

Determining the Number of Excavations Required to Confirm the Presence or Absence of SCC on a Pipeline Following an SCCDA Process

2016· article· en· W2549301392 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
TopicStructural Integrity and Reliability Analysis
Canadian institutionsDesjardins
Fundersnot available
KeywordsPipeline (software)ExcavationStress corrosion crackingPipeline transportCorrosionProcess (computing)USableCrackingStress (linguistics)Forensic engineeringEngineeringComputer scienceConstruction engineeringMetallurgyGeotechnical engineeringMaterials scienceMechanical engineeringWorld Wide WebComposite material

Abstract

fetched live from OpenAlex

The pipeline industry has been managing the threat of Stress Corrosion Cracking (SCC) for several years using the methods developed by NACE SCCDA [1] (Stress Corrosion Cracking Direct Assessment) and ASME B31.8S [2] standards. SCCDA is a widely accepted tool for assessing the threat of Stress Corrosion Cracking in pipelines. The process utilizes data from direct examinations at excavations to validate the process as well as to address existing SCC anomalies, if found. However, neither the recommended practices nor the literature provide a usable and practical method for determining the number of excavations necessary for the excavation program based on observed results. To address this question, the Pipeline Research Council International (PRCI) sponsored a project to develop a statistically defendable procedure to determine the number of excavations which would be required to validate the SCCDA process and confirm either the presence or absence of SCC.

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)0.001
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 score0.278
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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