Correction functions for soil–water characteristics curves extending the principles of thermodynamics
Bibliographic record
Abstract
The soil–water characteristics curve (SWCC) that is typically measured from laboratory tests is fit using mathematical models that are based on capillary law and used along with the saturated soil properties for predicting the hydro-mechanical behavior of unsaturated soils. Such SWCC models are valid for coarse-grained soils. However, the same models are also extended for modeling the SWCC of fine-grained soils over suction range from 0 to 106 kPa with a correction function. Due to this reason, SWCC models with correction functions have limitations in rigorous analyses of complex thermo-hydro-mechanical-chemo (THMC) behaviors of unsaturated soils, especially in the high suction range. In the present study, a correction function is proposed for modeling the SWCC behavior using two widely models based on a theoretical framework extending the principles of thermodynamics. The relationships between the traditional and the proposed correction functions are discussed. Finally, comparisons are provided between the proposed and the traditional correction functions on the SWCC behavior to highlight their differences. In addition, the effects of temperature and salinity on the SWCC with correction functions are also summarized. The proposed correction function is a valuable tool for rigorous analyses and reliable prediction of the complex THMC behaviors of unsaturated 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".