Experimental Investigation on Thermal and Electrical Properties of Binary Soil Mixtures
Bibliographic record
Abstract
This study investigates the effect of the percentage (f) of finer particles on thermal and electrical properties of binary soil mixtures via well-controlled laboratory tests. Binary mixtures of a fine silica sand (Ottawa F-55 sand) and a medium-coarse sand (ASTM 20/30 sand) were deposited at eight different mixing ratios in a custom-built soil box via air pluviation. After deposition, a thermal needle was inserted into the dry mixture sample to measure thermal conductivity. The thermal needle was then removed, and the mixture sample was saturated with a 0.1% NaCl solution. After completion of the saturation process, electrical resistivity and thermal conductivity of the saturated mixture sample were measured using a Nilsson meter and a thermal needle, respectively. Results from a series of laboratory tests showed that the void ratios of the binary soil mixtures initially decreased with the increasing percentage of finer particles and achieved the densest condition at f = 30%, but further increase in f led to an increase of void ratio. Both thermal conductivity and electrical resistivity increased as f increased, peaked at about f = 30%–50%, and then decreased. Also, the thermal conductivity in the saturated condition was about 8.3 times that in the dry condition.
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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".