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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 OpenAlex
S. Roorda, J. P. Clancy, Jonathan Bellemare, Simon Laliberté-Riverin

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.

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.

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)0.000
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 score0.265
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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