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Record W2103374533 · doi:10.1142/s021951941350019x

MICRO-FINITE ELEMENT MODELING OF WRINKLE FORMATION FOR CELL LOCOMOTION APPLICATIONS

2012· article· en· W2103374533 on OpenAlexafffund
Hadi Mohammadi, Walter Herzog, Kibret Mequanint

Bibliographic record

VenueJournal of Mechanics in Medicine and Biology · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsWestern UniversityUniversity of Calgary
FundersAlberta Innovates - Health Solutions
KeywordsMechanobiologyFinite element methodWrinkleStiffnessMaterials scienceStiffness matrixMatrix (chemical analysis)MembraneStructural engineeringComposite materialEngineeringAnatomyChemistry

Abstract

fetched live from OpenAlex

We developed a generic numerical procedure using the finite element method (FEM) for modeling wrinkles on an elastic thin membrane. The geometric characterization of wrinkles formed in an elastic membrane, which is caused by a crawling cell, may help in understanding the forces acting between a cell and its compliant substrate. In this study, the equivalent nodal forces, obtained from the virtual changes in the element internal energy, were differentiated to calculate the stiffness matrix. Micro-wrinkles can be computed as a function of membrane thickness which affects the stiffness matrix. This model may be used for applications in cell mechanobiology.

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)0.001
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 score0.920
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.296
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2012
Admission routes2
Has abstractyes

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