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Record W2158521191 · doi:10.1002/pen.21636

Application of digital image correlation method to biogel

2010· article· en· W2158521191 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

VenuePolymer Engineering and Science · 2010
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDigital image correlationMaterials scienceCrossheadUltimate tensile strengthComposite materialPoisson's ratioWork (physics)Strain (injury)Poisson distributionStress (linguistics)Tension (geology)PolynomialMathematical analysisMathematicsThermodynamicsPhysicsFlexural strength

Abstract

fetched live from OpenAlex

Abstract This study adopts the digital image correlation (DIC) method to measure the mechanical properties under tension in agarose gels. A second polynomial stress–strain equation based on a pore model is proposed in this work. It shows excellent agreement with experimental data and was verified by finite element simulation. Evaluation of the planer strain field by DIC allows measurement of strain localization and Poisson's ratio. At high stresses, Poisson's ratio is found to exceed the standard assumption of 0.5 which is shown to be a result of pore water leakage. Local failure strains are found to be approximately twice those determined by crosshead displacements. Viscous properties of agarose gels are investigated by performing the tensile tests at various loading rates. Increases in loading rate do not cause much difference in the shape of stress–strain curves, but result in increases in ultimate stress and strain. POLYM. ENG. SCI., 50:1585–1593, 2010. © 2010 Society of Plastics Engineers

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.224

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 head0.004
GPT teacher head0.216
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