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Record W4405382184 · doi:10.1061/jpsea2.pseng-1711

Visualized Analysis of Mapping Knowledge Domains for Oil and Gas Pipelines Failure Research

2024· article· en· W4405382184 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Pipeline Systems Engineering and Practice · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportPetroleum engineeringFossil fuelEngineeringEnvironmental scienceForensic engineeringEnvironmental engineeringWaste management

Abstract

fetched live from OpenAlex

Failure analysis is a vital technique that is intended to strengthen the integrity management of oil and gas pipelines. The significance is to reduce economic losses and avoid or minimize major failure incidents. The aim of this study is to use bibliometric methods to research 2,533 papers retrieved from the Web of Science database spanning from 2004 to 2023. The analysis conducted using VOSviewer, focused on temporal trends, geographic distribution, major organizations, leading authors, journal co-citations, and literature co-citations. The objective is to uncover research hotspots and frontiers, providing insights to advance failure analysis and prevention techniques. The findings revealed a substantial surge in the number of papers related to failure analysis, escalating from 16 in 2004 to 190 in 2023, indicating an overall exponential growth trend. This growth has been most pronounced over the past 8 years. Noteworthy contributors to this field include China, the USA, Canada, England, and Iran, with Iran, Australia, and Italy exerting significant impact. In addition, the top three research producers are all from institutions or universities located in China. The journals Engineering Failure Analysis, International Journal of Pressure Vessels and Piping, and Journal of Loss Prevention in the Process Industries exhibit the highest publication numbers. Significantly, Journal of Loss Prevention in the Process Industries and Gas Science and Engineering emerge as influential and highly regarded publications within this field. The study revealed that while the foundational theory and research framework in oil and gas pipeline failure have crystallized, a plethora of research directions and cutting-edge branches continue to emerge. Notably, the study of failure possibilities and behavior through Bayesian networks, failure characterization analysis, and finite element methods have emerged as the primary development directions and research hotspots. In terms of innovation, the application of bibliometric methods has enhanced the capacity to handle extensive literature databases and conduct network analyses. This study furnishes a theoretical foundation and guidance for the advancement of failure analysis and prevention techniques in the field of oil and gas pipelines.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.364
Teacher spread0.323 · 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