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Construction and consideration of integrated standard system for integrity management of oil and gas storage and transportation assets

2022· article· en· W6899584252 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldComputer Science
TopicDiverse Research and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationIntegrity managementTechnical standardAsset (computer security)Asset managementManagement systemQuality management systemInternational standardStandard system

Abstract

fetched live from OpenAlex

The construction of standard system by Chinese enterprises is characterized by the repeated and crossing contents, as well as multiple reference levels, of the single technical standards and the standards formulated by the standards committees of various disciplines. Thus, it is prone to causing the problems such as poor connection of standard content, great difference in requirements of implementation, and uneven development in the long-term application. In order to implement the National standardization development program and the strategic deployment of the PipeChina, the construction of PipeChina-featured integrated standard system for integrity management of oil and gas storage and transportation assets was practiced through combing the relevant laws and regulations, the national and the industry standards on integrity management of assets, with reference to the relevant Canadian national standards and the best practice results of the well-known foreign energy companies in the internal construction of integrated standards. Specifically, the integrated standard framework for integrity management of assets was built, and the preparation principles of integrated standards were established, including the unified object recognition, technical requirements and business management. The swim-lane flow chart for quality control of integrated standards was created. In addition, the integrated standards covering the integrity management of assets were formulated, including the general specifications for integrity management of assets, and the codes for integrity management of oil and gas pipelines. In this way, the problems of coordination, applicability and advancement of standards were effectively solved. In general, the research results could provide powerful guarantee for the realization of digital guidance of asset integrity management business objects and the operational activities.

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)0.006
metaresearch head score (Gemma)0.007
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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.163
GPT teacher head0.488
Teacher spread0.325 · 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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