Estimation of the water retention curve for unsaturated clay
Bibliographic record
Abstract
Mir Mohammad Hosseini, S. M., Ganjian, N. and Pashang Pisheh, Y. 2011. Estimation of the water retention curve for unsaturated clay. Can. J. Soil Sci. 91: 543–549. Extensive laboratory tests are essential in order to determine the soil water retention curve, defined as the relationship between water content and suction, in an unsaturated soil. These laboratory tests are usually costly and time consuming. Moreover, for most practical problems, it has been found that approximate unsaturated soil properties are adequate for analysis. Thus, empirical procedures for predicting unsaturated soil parameters would be invaluable. The water retention curve can be estimated using soil properties to avoid the costs of experimental methods. Estimation of the water retention curve based on index properties is highly desirable due to its simplicity and low cost. Here, a model for the estimation of the soil water retention curve for fine soils is introduced, which takes the plasticity index and fine content into account, and is based on the Van Genuchten and Fredlund-Xing equations. The proposed equations are validated by comparing measured and simulated results. The curves predicted with these models were found to be in good agreement with the experimental results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".