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Record W1023820740 · doi:10.1520/stp17984s

Strain-Aging Susceptibility of a Steel Repair Weld Using Internal Friction

2009· book-chapter· en· W1023820740 on OpenAlexaff
Z-L. Pan, I.G. Ritchie, N BAILEY

Bibliographic record

VenueASTM International eBooks · 2009
Typebook-chapter
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMaterials scienceImpurityInternal frictionDynamic strain agingCarbideStrain (injury)WeldingMetallurgyManganeseNitrideDislocationPrecipitationComposite materialStrain rateChemistry

Abstract

fetched live from OpenAlex

Measurements of the concentrations of free impurity interstitials in ferritic steels are of considerable importance in the evaluation of the strain-aging susceptibility of the steels. Traditionally, determination of interstitial concentrations has been carried out by measurement of the heights of the carbon and nitrogen Snoek peaks using internal friction techniques. However, in the case of steels containing significant concentrations of nitride or carbide formers in substitutional solid solution, pre-precipitation clustering of free impurity interstitials around the substitutional impurities leads to extraordinary Snoek peaks. Such is the case in steel repair welds containing 1.5% by weight manganese. Nevertheless, by studying both the Snoek spectrum and the interaction of the impurity interstitials with dislocations, the susceptibility to strain aging can be assessed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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