Modeling of the dielectric properties of biological tissues within the histology region
Bibliographic record
Abstract
The dielectric properties of biological tissues characterize the interaction of electromagnetic fields with the human body. As such, accurate knowledge of these properties is vital to the design and development of electromagnetic medical technologies. Despite their importance, the reported dielectric properties of key tissues have been inconsistent across studies. The natural heterogeneity of tissue has been identified as a contributing factor to these inconsistencies. In order to attribute dielectric properties to heterogeneous tissues, histological analysis is conducted to determine which tissue types contribute to the dielectric measurement. However, the Histology Region, i.e., the volume of the tissue sample that undergoes histological analysis, and the region corresponding to the dielectric measurement, has not been well-defined. Thus, instead of reducing uncertainties, more questions have been raised about the accuracy of data: if the Histology Region is not identified correctly, then the corresponding dielectric measurement does not represent the actual tissues involved. In this work, we examine the longitudinal extent of the Histology Region (i.e., the histology depth) for various heterogeneous samples composed of phantoms and biological tissues. This study highlights the fact that the relationship between the volume of tissue in a sample and the contribution of that tissue to the measured dielectric properties is not linear. Assuming that this relationship is linear may be a significant source of error in dielectric data. Further, we model, for the first time, the nonlinear relationship between the contribution of individual tissues to the dielectric measurement and the volume that each tissue occupies within the bulk sample. This work enables prediction of the permittivity of a sample with longitudinal heterogeneities, given knowledge of the constituent tissues of the sample, and provides the basis for modeling of all types of heterogeneities. These results will contribute to the minimization of uncertainties in the dielectric measurement of heterogeneous 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".