Application of Multiphase Dielectric Mixing Models for Understanding the Effective Dielectric Permittivity of Frozen Soils
Bibliographic record
Abstract
The time domain reflectometry (TDR)–measured effective permittivity in frozen soil conditions is affected by many complex factors including bound water effects on soil water permittivity, phase changes, soil microstructure and relative positions of soil constituents with respect to each other. The objective of this study was to improve understanding of some of the factors affecting the effective permittivity of frozen soils through the use of dielectric mixing models. Published datasets and frozen and unfrozen soil data measured on western Canadian soils were investigated with multiphase discrete and confocal ellipsoid models available in the literature. The results revealed that adjusting model parameters allowed the mixing models to describe the frozen soil permittivity equally well when bound water effects and temperature‐dependent water permittivity effects were included or not included. Measurement of freezing and thawing curves on western Canadian soils showed significant hysteresis and some mechanisms for this observed hysteresis and its influence on the interpretation of published datasets are discussed. When independent measurements of liquid water, ice and effective permittivity are available, it is possible to find one set of model parameters that reasonably predict effective permittivity for both frozen and unfrozen conditions. In frozen soils the predictive capability of the models is constrained to scenarios where the initial water content prior to freezing (i.e., the total water content) in the sampling volume is constant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".