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Record W4210698904 · doi:10.1016/j.jpse.2022.01.003

An overview on pipeline steel development for cold climate applications

2022· article· en· W4210698904 on OpenAlexafffund
Enyinnaya Ohaeri, Jerzy A. Szpunar

Bibliographic record

VenueJournal of Pipeline Science and Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCold climatePipeline (software)CrackingExtreme ColdEnvironmental scienceForensic engineeringMaterials scienceEngineeringMechanical engineeringGeologyClimatologyComposite material

Abstract

fetched live from OpenAlex

For some decades, the resources within the northern hemisphere have been studied for possible exploration. The need for reliable infrastructures in such extreme cold climatic condition is constantly on the rise. There is an imminent need to develop pipeline steels that can retain good characteristics under extremely low temperature. The focus of this review is to evaluate the basic requirements for producing steels designated for application in extreme cold polar regions. This study includes construction steels and the high strength pipeline steel grades used in sub-zero temperature applications. The emphasis is on the role of mechanical properties, chemical composition, and microstructure in designing steels for cold region. How these factors influence failure is critical, especially in terms of cracking behavior. Therefore, additional details about the synergy between low temperature and corrosive degradation are also discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.028
GPT teacher head0.263
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2022
Admission routes2
Has abstractyes

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