A Unified Model for the Soil Freezing Characteristic Curve Based on Pore Size Distribution and Principles of Thermodynamics
Bibliographic record
Abstract
Abstract The soil freezing characteristic curve (SFCC) is used as a tool for interpreting various properties of frozen soils such as hydraulic conductivity, volume change, and shear strength. Existing SFCC models are commonly based on empirical relationships, pore size distribution (POSD), or adaptations of the Soil‐Water Characteristic Curve (SWCC). Empirical models often lack a theoretical foundation, limiting their general applicability. Experimental evidence suggests that matric suction at a given temperature is affected by freezing rate, raising concerns about the suitability of SWCC‐based approaches for predicting SFCC. POSD‐based models are typically restricted to non‐saline soils, while current methods for saline soils primarily rely on SWCC‐based models, incorporating osmotic suction and iterative calculations to estimate unfrozen water content, which complicates practical applications. This study presents a unified SFCC model by transforming pore size into pore volumes using the POSD function (Weibull function) and extending the Gibbs‐Thomson equation to account for solute effects. The model eliminates the need for iterative calculations by deriving a freezing point depression equation that links the effects of confinement and solute concentration to pore size. Validations of the proposed unified SFCC model against experimental data demonstrates its accuracy in predicting unfrozen water content across different soil types and solute concentrations. Finally, the model's ability to simulate thermal‐hydraulic processes using a freezing column experiment is promising for its application in practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".