Effect of Physiologically Relevant Dehydration on the Dielectric Properties of Ground Beef
Bibliographic record
Abstract
ABSTRACT Readily available animal tissue, such as ground beef, is a convenient material to represent the dielectric properties of biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materials depends on the accurate characterization of their properties. In this work, the effect of physiologically relevant levels of dehydration on ex vivo tissue samples is quantified while controlling for variation within and between samples. Seven commercial ground beef samples (90% lean muscle, 10% fat) are dehydrated from 0.0% to 7.0% in 1.0% increments by weight. Dielectric measurements are collected using a conventional dielectric probe technique from 0.2 to 6 GHz. A linear mixed‐effects model is used to control for within‐ and between‐sample variation while modeling the effect of dehydration and dispersion across frequency. Significant () changes are noted in both permittivity and conductivity due to sample dehydration. For a 1% change in weight due to dehydration, changes in permittivity (5.1%–5.6%) and conductivity (3.2%–5.7%) are reported. These changes are important for the use of large muscle‐based phantoms in microwave sensing and imaging validation, as well as the feasibility of microwave hydration assessment. The statistical model used here can be applied to similar research questions and can augment existing frameworks for reporting dielectric measurements.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".