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Record W1516502686 · doi:10.3233/sfc-2010-0105

Automated hexahedral mesh generation of complex biological objects

2010· article· en· W1516502686 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

VenueStrength Fracture and Complexity · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHexahedronComputer scienceMesh generationGeologyEngineeringFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

This paper describes the development of a new automatic mesh generator for complex biological objects based on voxel hexahedral meshing (VHM). The system produces hexahedral meshes with multiple material classifications from three-dimensional (3D) computed tomography (CT) datasets. The quality of the VHM meshes is improved using a series of mesh connectivity improvement algorithms, leading to the application of a new and novel boundary smoothing algorithm. The boundary smoothing algorithm classifies boundary nodes into different groups based on the configuration of their neighbouring elements: this provides a varying compromise between smoothness and element distortion. The high anatomic detail of which the method is capable is shown for meshes of single and mixed material types. This system illustrates how to harness the speed and simplicity of VHM as an initial mesh generator while simultaneously improving the quality of those meshes with additional meshing techniques. MATLAB code for this software is freely available for research purposes upon request.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.491

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 head0.069
GPT teacher head0.296
Teacher spread0.228 · 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