Analysis of the complex effective permittivity of a heterogeneous sample by the finite-difference time-domain method
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Bibliographic record
Abstract
Numerical results of the complex effective permittivity and DC conductivity of heterogeneous samples with random constituents’ distribution are analyzed to search for a suitable mixture law. The calculation method consists of the simulation of a time-domain reflectometry experiment by means of the finite-difference time-domain (FDTD) algorithm. The permittivity and conductivity of several series of samples with their constituents distributed at random are calculated. Numerical results, as a function of the volume fractions of the mixture’s constituents, are compared with analytical mixture laws. For lossless binary media, it is found that both the Bruggeman law and a weighted geometric mean provide a good agreement. The last one can be generalized to composites with more than two constituents, with good results. On the other hand, for lossy binary mixtures it is shown that the weighted geometric mean of the constituents’ complex permittivities matches the numerical results obtained for the effective complex permittivity of the mixture.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it