Comparison of Soil‐Freezing and Soil‐Water Characteristic Curves of Two Canadian Soils
Bibliographic record
Abstract
Core Ideas The similarity between SFCC and SWCC, and hysteresis of SFCC are reviewed. The SFCC and SWCC of two fine‐grained soils are measured and analyzed. No quantitative similarity is found between the measured SFCC and SWCC. Several concerns regarding the similarity between SFCC and SWCC are discussed. The drying–wetting and freezing–thawing cycles significantly influence the soil pore water in the vadose zone in permafrost and seasonally frozen regions. The soil‐freezing characteristic curve (SFCC) describes the relationship between unfrozen water content and subzero temperature in a soil at frozen condition. Several studies suggest that the SFCC of a frozen saturated soil is similar to soil‐water characteristic curve (SWCC), which describes the relationship between water content and suction for a soil under unfrozen unsaturated condition. In the present study, the similarity between SFCC and SWCC, and possible reasons for the hysteresis of SFCC are succinctly reviewed. The SFCC and SWCC of two Canadian soils were measured and critically interpreted to understand the fundamental behavior of SFCC in comparison with the SWCC. The observed hysteresis of SFCC for the two soils was mainly associated with the supercooling of pore water. The measured SFCC and SWCC of the two soils show quantitative dissimilarity rather than similarity. This may be attributed to the experimental limitations and possible fundamental differences between drying–wetting and freezing–thawing processes. In addition, several concerns regarding the similarity between SFCC and SWCC are discussed. The present study highlights that rigorous investigations are required for better understanding the SFCC to facilitate its use for cold‐region engineering practice applications.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".