Accurate Technique for Measuring Electrical Permittivity of Biological Tissues at Low Frequencies and Sensitivity Analysis
Bibliographic record
Abstract
This paper presents an accurate technique for measuring electrical permittivity (EP) of small biological tissue specimens at low frequencies while measurement sensitivity to specimen geometry and tissue specimen inhomogeneity is investigated. Accurate EP measurement of various biological tissues, including healthy and pathological tissues, can pave the way for a wide range of medical applications. This novel technique utilizes a high precision hardware for impedance measurement of a capacitive structure formed by two conductive plates and the tissue specimen as its dielectric. The capacitance part of the measured impedance is processed using an inverse finite element framework to determine the tissue electrical permittivity. This framework considers specimen's accurate geometry and boundary conditions. After successful validation, the technique was employed to measure electrical permittivity of several specimens of bovine heart, liver and bone tissues. The proposed technique is reliable and is expected to offer improved measurement accuracy and repeatability of electrical permittivity of multi-layered tissue specimens, especially at low 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".