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Record W3203246321 · doi:10.1080/15376494.2021.1979138

Extracting material parameters of silicone elastomers under biaxial tensile tests using virtual fields method and investigating the effect of missing deformation data close to specimen edges on parameter identification

2021· article· en· W3203246321 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

VenueMechanics of Advanced Materials and Structures · 2021
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsOntario Tech University
FundersNatural Science Foundation of Anhui Province
KeywordsDigital image correlationMaterials scienceDeformation (meteorology)Ultimate tensile strengthNoise (video)ElastomerComposite materialSiliconeIdentification (biology)Missing dataScalingStructural engineeringImage (mathematics)Computer scienceArtificial intelligenceGeometryMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

In this work, virtual fields method (VFM) is applied to extract the constitutive parameter of silicone elastomers under equi-biaxial and general biaxial tests; the influence of missing deformation data near specimen edges and noise introduced by digital image correlation (DIC) on parameter identification using VFM is investigated. The results indicate that the negative impact of the missing data and noise can be mitigated by scaling the load applied to the specimen boundary properly and extracting parameters based on the deformation fields of all loading steps through the least square method.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.034
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.054
GPT teacher head0.329
Teacher spread0.275 · 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