A 3-D Hybrid Finite Element Model to Characterize the Electrical Behavior of Cutaneous Tissues
Bibliographic record
Abstract
Finite element modeling of the skin is useful to study the electrical properties of cutaneous tissues and gain a better understanding of the current distribution within the skin. Such an epithelial finite element model comprises extremely thin structures like cellular membranes, nuclear membranes, and the extracellular fluid. Meshing such narrow spaces considerably increases the number of elements leading to longer computing time. This also greatly reduces the number of epithelial cells that can be assembled before reaching computing limitations. To avoid the problem of meshing extremely narrow spaces while unnecessarily increasing the number of elements, we present a new hybrid modeling approach to develop a 3-D finite element model of the skin. This skin model comprises all skin layers, different lesion types, and a complete electrode model. It is used to analyze the complex electrical behavior of normal and malignant skin tissues. The current distribution within this model is also simulated to assess the depth of field achievable by an electrical impedance tomography system at different operating frequencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".