MétaCan
Menu
Back to cohort
Record W2041531034 · doi:10.1115/1.3110017

Target Reliability Levels for Design and Assessment of Onshore Natural Gas Pipelines

2009· article· en· W2041531034 on OpenAlexaff
Maher Nessim, Wenxing Zhou, Joe Zhou, Brian Rothwell

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
FundersHealth and Safety ExecutiveBP Exploration Operating Company Limited
KeywordsReliability (semiconductor)Pipeline transportRisk analysis (engineering)Risk assessmentSet (abstract data type)Reliability engineeringPipeline (software)Function (biology)Computer scienceNatural gasEngineeringBusinessEnvironmental engineeringComputer security

Abstract

fetched live from OpenAlex

This paper proposes a set of reliability targets that can be used in the design and assessment of onshore natural gas pipelines. The targets were developed as part of a PRCI-sponsored project that aims to establish reliability-based methods as a viable alternative for pipeline design and assessment. The proposed targets are calibrated to meet risk levels that are considered widely acceptable. The proposed criteria are based on a detailed consideration of both societal and individual risk criteria. Two societal risk criteria were considered: the first based on a fixed expectation of the number of fatalities and the second based on a risk aversion function as characterized by a F/N relationship. Societal risk criteria were calibrated to match or exceed the average safety levels implied by current codes. Individual risk criteria were based on published tolerable levels. The target reliability levels corresponding to the three criteria are presented and a recommended set of targets is presented.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.285
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

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 designTheoretical or conceptual
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

Citations64
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicStructural Integrity and Reliability AnalysisFrench-language works237,207