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Record W2900357607 · doi:10.1115/ipc2018-78413

Analysis of the National Energy Board Pipeline Integrity Performance Measures Data

2018· article· en· W2900357607 on OpenAlexaff
Joe Paviglianiti, Sarah Shaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCanada Energy Regulator
Fundersnot available
KeywordsLaggingPipeline (software)Pipeline transportIntegrity managementRisk analysis (engineering)Computer scienceStakeholderBusinessEngineeringAccounting

Abstract

fetched live from OpenAlex

The National Energy Board’s (NEB or the Board) top priorities are the safety of people and protection of the environment. NEB-regulated pipelines have a very good safety record; however, the NEB noticed an increased trend in some types of incidents. Therefore, after considerable stakeholder consultation, in March 2012 the NEB started requiring NEB-regulated companies to report annually on new pipeline performance measures. These performance measures were developed and introduced to promote continual improvement in the management of pipelines by allowing companies to compare their results with industry aggregate numbers. In addition, these metric results allow the NEB to both evaluate and demonstrate that pipeline companies are effective in managing pipeline safety and protection of the environment. The NEB requires all regulated companies to report on incidents, such as releases of substances and serious injuries. Pipeline performance measures data provides the Board additional information such as lagging and leading indicators. These lagging indicators provide an historical view of company performance while the leading indicators provide forward looking data of potential future events. The NEB is of the view that an amalgamation of leading, lagging and qualitative measures can provide an overview of company effectiveness in meeting foundational management system program objectives. This paper examines four years of reported integrity related performance and integrity inspection data to evaluate trends in activities taken by companies to maintain safe pipelines. This paper will briefly discuss the challenges encountered when developing the measures, obtaining consistent data and evaluation of the data to identify trends. The paper will conclude by summarizing select results of the integrity performance measures and integrity inspection information data and discuss any potential future actions related to the pipeline performance integrity measures.

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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0100.018
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.046
GPT teacher head0.270
Teacher spread0.223 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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