Using Distmesh as a mesh generating tool for EIT
Bibliographic record
Abstract
We present a collection of automatic mesh generation software tools for electrical impedance tomography (EIT) finite element models (FEMs). Creating an image in EIT involves retrieving conductivity distribution from surface electrode measurements. The initial forward problem of this process requires estimates of electric potential according to the current sources. With non-trivial geometries, this task is difficult analytically and demands a numerical method such as FEM. The areas nearest to electrodes have the highest current density and are the most significant measurement points. For our FEM model, these areas should have the greatest mesh density for the purposes of usefulness and accuracy. The research focuses on Distmesh, an open source MATLAB mesh generator, for creating mesh shapes with non-uniform mesh densities for EIT software. The inherent simplicity of the Distmesh code was the rationale for its evaluation. A mesh shape is defined from specifying its signed distance function and its mesh size distribution is defined with an element length function. We created circular meshes with mesh refinement towards endpoints of distributed electrodes. In three dimensions, cylinders were produced with mesh refinement towards rings of points for each electrode. Finally, to accommodate organic shapes, meshes for closed contours were created using elliptic Fourier descriptors. Because Distmesh was not designed for efficiency, the generation of well-defined meshes is fairly slow. For the contours, Distmesh forms reasonable meshes but is unable to improve node locations upon successive iterations. Distmesh is not a suitable mesh generator for EIT software unless its code is optimized for speed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".