Investigation of Histology Region in Dielectric Measurements of Heterogeneous Tissues
Bibliographic record
Abstract
The dielectric properties of tissues are the key parameters in electromagnetic medical technologies. Despite the apparent simplicity of the dielectric measurement process, reported data have been inconsistent for heterogeneous tissues. Dielectric properties may be attributed to heterogeneous tissues by identifying the tissue types that contributed to the measurement through histological analysis. However, accurate interpretation of the measurements with histological analysis requires first defining an appropriate histology region to examine. Here, we investigate multiple definitions for the probe sensing depth and uniquely calculate this parameter for measurements with a realistic range of tissues. We demonstrate that different sensing depth definitions are not equivalent, and may introduce error in dielectric data. Last, we propose an improved definition, given by the depth to which the probe can detect changes in the tissue sample, within the measurement uncertainty. We equate this sensing depth with histology depth, thus supporting the need of having the tissue region that contributes to the dielectric data be the same as that which is analyzed histologically. This paper demonstrates that, for these tissues, the histology depth is both frequency and tissue dependent. Therefore, the histology depth should be selected based on the measurement scenario; otherwise, inaccuracies in the data may result.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".