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Record W2900220099 · doi:10.1115/ipc2018-78672

Strengthening the Current Class Location Designation System

2018· article· en· W2900220099 on OpenAlexaff
Diana Willson, Iain Colquhoun, Dean Carnes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCanadian Natural ResourcesCanada Energy Regulator
Fundersnot available
KeywordsReliability (semiconductor)Class (philosophy)PipingPipeline (software)Pipeline transportComputer scienceInterimRange (aeronautics)Reliability engineeringPopulationTransport engineeringEngineeringCivil engineeringGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

CSA Z662, Oil and gas pipeline systems, defines class location as “a geographical area classified according to its approximate population density and other characteristics that are considered when designing and pressure testing piping to be located in the area.” In other words, the purpose of class location designations is to identify areas where specific measures are considered necessary to enhance public safety. Designations range from Class 1 (rural) to Class 4 (urban with high-rise buildings). The current class location framework relies mainly on a location factor (L) to represent reliability. Higher reliability is achieved by using more resistant pipe — that is thicker and/or stronger — to reduce the probability of failure from operational hazards, such as corrosion and mechanical damage caused by line strikes. Currently, the need for a particular level of reliability is driven principally by the number of people impacted. This paper discusses possible measures that can be implemented in the next edition of Z662 that, beyond requiring thicker pipe for certain products, will strengthen the class location designation system by considering the potential impact radius of an ignited gas pipeline rupture, as well as the occupancy and nature of buildings within assessment areas. The paper also discusses possible changes to improve environmental protection by introducing the concept of a designated geographical area (DGA) and associated requirements, enhancements to valve spacing requirements, and the handling of changes to class location designations for existing pipelines through interim measures and retroactivity.

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0200.016

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 head0.237
Teacher spread0.221 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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