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Record W4312528691 · doi:10.1115/ipc2022-87099

A Risk-Based Safety Class System for Onshore Pipelines

2022· article· en· W4312528691 on OpenAlexaboutno aff
Maher Nessim, Mark Stephens, Howard Yue

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportPipeline (software)HazardClass (philosophy)System safetyPetroleum engineeringEngineeringNatural gasRisk analysis (engineering)Computer scienceEnvironmental scienceCivil engineeringReliability engineeringWaste managementEnvironmental engineeringMechanical engineeringBusiness

Abstract

fetched live from OpenAlex

Abstract A consequence-based safety class system was developed as an alternative to the class location system currently used as the basis for defining the maximum allowable hoop stress in Canadian Standard Association’s Standard Z662. The system accounts for the key pipeline, service fluid, and right-of-way parameters affecting the safety and environmental impact of releases from pipelines transporting natural gas, low- and high-vapour-pressure liquid hydrocarbons, sour gas, carbon dioxide, multiphase, and oilfield water pipelines. This paper describes the safety class system and the simplified release consequence modelling approach developed to support its implementation. Companion papers provide more detail on the release hazard analyses used as a basis for consequence modelling, and the application of the safety class system as a basis for defining pressure design hoop stress factors that achieve acceptable and consistent public safety and environmental protection levels for all pipelines considered.

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)0.003
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.068
GPT teacher head0.352
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

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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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