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Record W2951274050 · doi:10.1002/srin.201900078

A Comparative Study of the Role of Hydrogen on Degradation of the Mechanical Properties of API X60, X60SS, and X70 Pipeline Steels

2019· article· en· W2951274050 on OpenAlexaff
K.M. Mostafijur Rahman, M.A. Mohtadi-Bonab, Ryan Ouellet, Jerzy A. Szpunar

Bibliographic record

Venuesteel research international · 2019
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceCathodic protectionUltimate tensile strengthDuctility (Earth science)ToughnessMetallurgyHydrogenHydrogen embrittlementCrackingScanning electron microscopeFracture (geology)Fracture toughnessComposite materialCorrosionCreepAnode

Abstract

fetched live from OpenAlex

Three grades of high strength pipeline steel (API X60, X60SS, and X70) are subjected to cathodic hydrogen charging to determine the susceptibility of each steel grade to hydrogen‐induced cracking (HIC). The specimens are subjected to tensile stress until failure to generate stress‐strain, ductility, and toughness data. The fractured specimens are analyzed using a scanning electron microscope equipped with an EDS detector to determine crack initiation sites for the investigated grades of steel. The expectation that the X60SS steel grade will perform better than other grades is not met, instead it is found that under the cathodic hydrogen charging condition, the X60SS is more susceptible than the other two steel grades with respect to loss of toughness, ductility, and ultimate tensile strength. The presence of Ca‐Al‐based inclusions is the most common along the fracture surfaces. Therefore, these inclusions also act as fairly strong trapping sites that under plastically strained conditions, they can result in fisheye features which severely impact the mechanical properties of the steel specimens.

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 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)0.002
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 score0.008
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.088
GPT teacher head0.361
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations31
Published2019
Admission routes1
Has abstractyes

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