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Record W2598307554 · doi:10.1115/1.4036217

Probabilistic Methods for Predicting the Remaining Life of Offshore Pipelines

2017· article· en· W2598307554 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

VenueJournal of Pressure Vessel Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCommunity Sector Council Newfoundland and LabradorIntecsea (Canada)Memorial University of Newfoundland
Fundersnot available
KeywordsPipeline transportIntegrity managementReliability (semiconductor)Risk analysis (engineering)Submarine pipelinePipeline (software)Probabilistic logicHazardReliability engineeringComputer scienceEngineeringConstruction engineeringForensic engineeringEnvironmental scienceBusinessEnvironmental engineering

Abstract

fetched live from OpenAlex

When offshore pipelines approach the end of their design life, their condition could threaten oil flow continuity as well as become a potential safety or environmental hazard. Hence, there is a need to assess the remaining life of pipelines to ensure that they can cope with current and future operational demand and integrity challenges. This paper presents a methodology for assessing the condition of aging pipelines and determining the remaining life that can support extended operation without compromising safety and reliability. Applying this methodology would facilitate a well-informed decision that enables decision makers to determine the best strategy for maintaining the integrity of aging pipelines.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.381
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.326
Teacher spread0.299 · 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