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Record W2070593057 · doi:10.1115/pvp2008-61646

Integral Mean of Yield Criterion in Design and Fitness for Service Assessment

2008· article· en· W2070593057 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.

Bibliographic record

VenueVolume 2: Computer Applications/Technology and Bolted Joints · 2008
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsMemorial University of NewfoundlandAtomic Energy (Canada)
Fundersnot available
KeywordsLimit (mathematics)Yield (engineering)Upper and lower boundsMathematicsLimit loadApplied mathematicsMathematical optimizationMathematical analysisStructural engineeringMaterials scienceEngineeringComposite materialFinite element method

Abstract

fetched live from OpenAlex

Mura’s variational formulation for determining limit loads, originally developed as an alternative to classical methods, is extended further by allowing the pseudo-elastic distributions of stresses to lie outside the yield surface provided they satisfy the “integral mean of yield” criterion. Consequently, improved lower-bound and upper-bound values for limit loads are obtained. The mα estimation limit load method, reference volume method and the fitness for service assessment procedure (including corrosion damage and thermal hot spot damage), are all applications and extensions of the “integral mean of yield” criterion.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.511

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 integrity0.0000.000
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 head0.021
GPT teacher head0.233
Teacher spread0.212 · 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