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Record W1531922497 · doi:10.1002/9781119019213.ch01

Pipeline Integrity Management Systems (PIMS)

2015· other· en· W1531922497 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
Typeother
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsMidstreamIntegrity managementPipeline transportPetroleum industryPipeline (software)Upstream (networking)Downstream (manufacturing)Management systemAsset managementEngineeringAsset (computer security)BusinessRisk analysis (engineering)Construction engineeringComputer securityComputer scienceOperations managementTelecommunicationsFinance

Abstract

fetched live from OpenAlex

Pipeline integrity management systems (PIMS) provide the overarching, integrated framework for effective pipeline asset management. Significant failures in both gas and liquid pipelines have made global headlines. There is no single correct “formula” for developing an integrity management system; however, this chapter outlines the fundamental basics of an effective management system that have been successfully integrated in companies across the world. Industry groups such as International Association of Oil and Gas Producers and the American Petroleum Institute (API) have developed guidance documents that can be used as additional references for developing management systems. The chapter covers downstream, midstream, and upstream oil and gas pipelines. It reviews the latest industry and regulatory documents pertaining to both safety management systems (SMS) and PIMS. The codes, standards, and regulations that govern the pipeline industry continue to change in response to lessons learned from industry failures.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.013
GPT teacher head0.206
Teacher spread0.193 · 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

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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