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Challenges and developments in pipeline weldability and mechanical properties

2013· article· en· W2002333893 on OpenAlexaff
C Liu, S.D. Bhole

Bibliographic record

VenueScience and Technology of Welding & Joining · 2013
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWeldabilityConsumablesWeldingPipeline transportMaterials scienceToughnessPipeline (software)Petroleum industryFossil fuelMetallurgyMechanical engineeringEnvironmental scienceEngineeringBusinessWaste management

Abstract

fetched live from OpenAlex

Recent economic and political events have further highlighted the need for new and strategically accessible sources of oil and gas. With the continually increasing demand for oil and gas, the requirement for pipeline steels with higher strength, toughness and weldability has been one of the most important factors driving the development of high strength pipeline steels, particularly with the oil exploration proceeding into arctic and deep sea regions, enhancing the weldability and mechanical properties of the new pipeline steels and weld consumables. Developments in the welding processes for manufacture and field welding are described in terms of process principles, equipment, consumables, weld quality, process economics and further developments. The increasing and changing requirement for weldability and mechanical properties in the heat affected zone and weld metal of pipeline welds are presented along with the reported solutions to the problems.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.041
GPT teacher head0.263
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

Citations47
Published2013
Admission routes1
Has abstractyes

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