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Record W2065480786 · doi:10.1002/cnm.1209

<i>r</i>‐Adaptation algorithm guided by gradients of strain energy density

2008· article· en· W2065480786 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

VenueInternational Journal for Numerical Methods in Biomedical Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSmoothingLaplace operatorIterated functionAlgorithmFinite element methodLaplacian smoothingEnergy (signal processing)MathematicsStrain energy density functionStrain energyStandard deviationConvergence (economics)Mathematical optimizationApplied mathematicsMathematical analysisMesh generationStructural engineeringEngineeringStatistics

Abstract

fetched live from OpenAlex

Abstract In this paper, a r ‐adaptation algorithm is presented. The algorithm is based on weighted Laplacian smoothing. In the proposed algorithm, the gradients of strain energy density are used as weight functions; Laplacian smoothing is iterated until the maximum deviation or standard deviation in mesh intensity is smaller than a prescribed value. Numerical results show that the algorithm is sensitive and robust. The algorithm can be extended to other finite element formulations by replacing strain energy density with its corresponding counterpart. Copyright © 2008 John Wiley &amp; Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.346
Teacher spread0.316 · 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