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Record W2746706218 · doi:10.1111/ffe.12700

Numerical verification of stress intensity factor solution for clamped single edge notched tension (SENT) specimens

2017· article· en· W2746706218 on OpenAlexaff
Chris Bassindale, Xin Wang, W. R. Tyson, Su Xu

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2017
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources CanadaCarleton University
FundersAustralian Government
KeywordsStress intensity factorFinite element methodEnhanced Data Rates for GSM EvolutionTension (geology)Materials scienceRange (aeronautics)Stress (linguistics)Function (biology)Structural engineeringPolynomialComposite materialMathematicsMathematical analysisEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Stress intensity factor solutions for clamped single edge notched tension (SENT) specimens, including a closed‐form function recently proposed by Zhu along with a function by CanmetMATERIALS referenced in the British Standard BS 8571, have been assessed. Solutions for an SENT specimen with a daylight‐to‐width ratio of 10 have been compared with new finite element results generated in this work to assess their accuracies. The results of this study show that the polynomial proposed by Zhu differs by no more than 0.23% compared with the numerical results over the range of 0.2 ≤a/W ≤ 0.7. The CANMET function differs by no more than 0.69% over the same range.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.248
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations15
Published2017
Admission routes1
Has abstractyes

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