Effect of freeze–thaw cycling on the soil‐freezing characteristic curve of five Canadian soils
Bibliographic record
Abstract
Abstract The frozen soil processes and their interaction with the environment in the vadose zone of cold regions is vital in both agricultural and engineering practice applications. In a frozen soil, unfrozen water and pore ice coexist. The relationship between the unfrozen water content and subzero temperature is widely known as the soil‐freezing characteristic curve (SFCC). The SFCC is a valuable tool for predicting the hydromechanical properties and for modeling the coupled thermal–hydraulic–mechanical–chemical process in frozen soils. In spite of its importance, the effect of freeze–thaw (F–T) cycling on SFCC has not been well investigated or understood. In this technical note, the effect of F–T cycles on the SFCC of five soils from cold regions of Canada were investigated. The SFCC (including both freezing and thawing branches) of the five soils for different F–T cycles were measured using frequency domain reflectometry (FDR) technique. The experimental results suggest that the effect of F–T cycles on the SFCC of the five soils is not significant. Such a behavior may be attributed to the destruction of soil structure during the saturation process. However, all the five soils’ SFCC exhibited hysteresis behavior for all the F–T cycles. The results of the study are valuable and contribute towards better understanding of the fundamental behavior of SFCC of various cold region soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".