Significance of heterogeneities in accurate dielectric measurements of biological tissues
Bibliographic record
Abstract
Accurate knowledge of the dielectric properties of biological tissues is necessary for the design and development of electromagnetic medical technologies; these properties quantify the accuracy and efficacy of system operations. Despite the pressing need, the dielectric properties reported in the literature have suffered from inconsistencies mainly attributed to differences in measurement procedures. In this work, a key source of uncertainty, heterogeneous tissue composition within the sensing region of the dielectric probe, is investigated for biological samples composed of porcine muscle and fat. In particular, the contribution of tissues within the sensing depth to measured dielectric data is quantified and the assumption of equal impact of all tissues within the sensing depth is examined. This study demonstrates quantitatively that tissues at different depths below the measurement site do not contribute proportionally to the measured properties, thus suggesting that new analysis methods need to be developed to account for heterogeneous tissue samples in dielectric measurement data. This improved understanding of how heterogeneous tissues within the sensing region affect dielectric measurements facilitates future studies to reduce uncertainty and improve the quality of collected dielectric data of biological tissues.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".