A theoretical method to relate the relative permittivity and thermal conductivity of sands
Bibliographic record
Abstract
A theoretical method to relate the electrical relative permittivity (dielectric constant) and thermal conductivity of sands is presented. Extensive previous work has been done to relate the physical state (void ratio, water content, and saturation percent) of sandy soils to the bulk thermal conductivity and bulk relative permittivity respectively. These parameters have always been observed isolated from each other. However, both the bulk thermal conductivity and bulk relative permittivity are primarily dependent on the same physical characteristics. Therefore, it should be possible to estimate the thermal conductivity of a soil based on the relative permittivity measured (or vice versa). The objective of this research is to show that this estimation is possible based on actual laboratory measurements. Thermal conductivity and relative permittivity measurements were conducted on prepared bench scale specimens of dry Ottawa Sand at varying density. Thermal conductivity was measured using a thermal needle technique and relative permittivity was measured using a Dynamax TH2O probe. Based on the data collected, there appears to be a linear relationship between the two properties. A correlation is proposed based on the data collected that allows for one to calculate thermal conductivity directly from the relative permittivity measurement. Based on the data collected it appears that the theory that these properties could be estimated from each other is valid for dry sand.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".