The Measurement of Unfrozen Water Content and SFCC of a Coarse-Grained Volcanic Soil
Bibliographic record
Abstract
ABSTRACT In frozen soils, a portion of pore water remains unfrozen due to the effects of capillarity, adsorption, and possibly solute. The variation of the amount of unfrozen water and ice in a frozen soil, which is primarily influenced by subzero temperature, has great impacts on the physical and mechanical behavior of the soil and is critical for broad applications ranging from engineering to climate change. In the present study, the various methods that have been used for determining unfrozen water (and ice) content are comprehensively reviewed. Their principles, assumptions, advantages, and limitations are discussed. It is noted that there is yet no perfect way to accurately quantify unfrozen water content in frozen soils. In addition, the soil-freezing characteristic curve (SFCC) of a typical volcanic soil sampled in the Hokkaido prefecture of Japan is investigated. The unfrozen water content of the prepared soil specimens was measured using a cheap moisture sensor, which is based on the frequency domain reflectometry technique. The temperature of the specimens was determined by a rugged temperature sensor. Different numbers of freeze-thaw (F-T) cycles and different freezing/thawing methods (i.e., one- and three-dimensional) were considered, and their effects on the SFCC were investigated. The experimental results suggest that neither the F-T cycles nor the freezing/thawing methods had significant influence on the measured SFCC. The presented comprehensive review and experimental investigations are of importance for both the scientific and engineering communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".