QUANTIFYING THE SOIL FREEZING CHARACTERISTIC CURVE IN LABORATORY AND FIELD SOILS
Bibliographic record
Abstract
The soil freezing characteristic curve (SFC) controls the hydraulic properties of soils and is especially crucial in understanding snowmelt infiltration and runoff, frost heave formation and thawing settlement in frozen soils. The SFC can be modelled by combining information from the soil moisture characteristic curve of unfrozen soils (SMC) with the Generalized Clapeyron Equation (GCE). While such an approach is straightforward and involves no additional free parameters, the resulting SFC is not always consistent with those observed in the laboratory and field. This study was therefore designed to obtain both laboratory and field data that quantifies the SMC and SFC for different soil textures and salinities and to compare the results with those obtained from the GCE and other alternative relationships. \n\nIn the laboratory, the SMC of a silica sand was measured using a Hydraulic Property Analyzer (HYPROP). The SFC of the same sand was measured using a series of column experiments with controlled total water content and pore-water salinity. In the field, data were collected from the St Denis National Wildlife Area (SDN), a mixed grassland cropped site in the Canadian prairies in Saskatchewan and the Boreal Ecosystem Research and Monitoring Sites (BERMS) Old Jack Pine (OJP) site in Saskatchewan, Canada. Three alternative models for the SFC were developed (capillary, salt exclusion, and the combined capillary salt models), and compared with observed data from the laboratory and field experiments.\n\nThe results show that the column experiments were successful in producing well-defined SFCs that matched expectations, where the form of the decrease in liquid water content with temperature was similar to the form of the SMC. Increasing the salinity resulted in enhanced freezing point depression as was expected. The field SFCs followed the same trend as those measured in the laboratory. The modelling results suggest that salinity is a dominant control on the SFC in real soils and that the combined capillary salt model is the most realistic of the three models considered in predicting liquid water content in frozen soils.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".