MétaCan
Menu
Back to cohort
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 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.
fundA Canadian funder is recorded on the work.

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.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.028
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