Measuring the Content of Unfrozen Water in Frozen Soil Based on Resistivity
Bibliographic record
Abstract
This study proposes a new method for calibrating the theoretical model of resistivity versus unfrozen water content in frozen soils. The method characterizes correlations between the soil mass resistivity and unfrozen water content in the frozen state by investigating the relation between resistivity and liquid water content in the drying state. The essential similarity between the soil mass freezing and drying processes is analyzed through the process and path of unfrozen (liquid) water reduction. The resistivity and unfrozen water (liquid water) content were correlated in soil samples with different particle sizes (clay, silt, and sandy soil) during freezing and drying. The test results showed that during freezing to temperatures below 5 °C (clay), 4 °C (silt), and 3 °C (sandy soil), the unfrozen water content thresholds of providing directional migration channels for conducting particles were 17%, 14%, and 13%, respectively. During drying, the threshold water contents of clay, silt, and sandy soil were 15.35%, 14.87%, and 14.34%, respectively. The correlation between the soil resistivity ( ρ D ) and unfrozen water content ( θ u ) in the freezing process can be expressed based on that between the soil resistivity and liquid water content in the drying state. Thus, the theoretical model of unfrozen water content can be calibrated by conducting a resistivity test under the drying condition. This new method is suitable for model calibrations of electrical resistance tomography for engineering applications in cold regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".