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Record W4200365684 · doi:10.3233/jnr-210027

Exploring the use of neutrons to detect hydrogen embrittlement in high strength steel

2021· article· en· W4200365684 on OpenAlexaff
S. Roorda, J. P. Clancy, Jonathan Bellemare, Simon Laliberté-Riverin

Bibliographic record

VenueJournal of Neutron Research · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsPolytechnique MontréalMcMaster UniversityBrockhouse Institute for Materials ResearchUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceHydrogen embrittlementHydrogenEmbrittlementNeutronScatteringNeutron scatteringSmall-angle neutron scatteringMetallurgyNuclear physicsPhysicsOpticsCorrosion

Abstract

fetched live from OpenAlex

With the aim of exploring neutron techniques for the non-destructive detection of hydrogen in embrittled steel, three sets of steel samples were studied with neutron scattering: Ni coated, Cd coated, and Cr coated. Each set contained a non-embrittled or low-hydrogen concentration reference and one or two embrittled and high-hydrogen concentration samples. It is observed that the incoherent scattering, when normalized by the intensity of the Bragg peak, is significantly higher for high-hydrogen concentration or embrittled samples than in the reference. Although the difference is small, this represents a non-destructive technique of detecting hydrogen embrittlement. Neutron radiography, and inelastic or small-angle scattering could not distinguish between embrittled and reference samples.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.234
GPT teacher head0.366
Teacher spread0.132 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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