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Record W3110977783 · doi:10.1115/1.4049320

Effect of Cold-Rolling on Hydrogen Diffusion and Trapping in X70 Pipeline Steel

2020· article· en· W3110977783 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 Engineering Materials and Technology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMisorientationHydrogenDiffusionMaterials scienceGrain boundaryMicrostructureTrappingPermeationGrain boundary diffusion coefficientMetallurgyPermeability (electromagnetism)Intensity (physics)ChemistryThermodynamicsOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract In this investigation, we prepared samples with five different grain misorientations by cold-rolling an X70 pipeline steel plate. The hydrogen permeation and hydrogen visualization experiments were used to compute the diffusion parameters and to reveal the diffusion path in steel samples. The dual-polarized permeation experiment allowed us to show that permeability and effective diffusion coefficient were decreased with an increase in misorientation. Hence, the total and irreversible trapping sites were also raised with the extent of deformation in the steel. On the other hand, the visualization study permitted us to show that hydrogen diffusion intensity changes within the microstructure. The diffusion intensity increases in the order of non-deformed grains, grain boundaries, and deformed grains with deformed grains as the easiest path for hydrogen diffusion.

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)0.001
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 score0.001
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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