Water-Retention Curves of Coarse Soils Without Organic Matter: Improved Data for Improved Predictions
Bibliographic record
Abstract
Abstract It is difficult to obtain a reliable water-retention curve (WRC) for coarse-grained soils without organic matter. As a result, the field behavior of draining layers in engineering problems may be poorly predicted. The main goal of this paper is to develop predictive methods for the WRC of coarse-grained soils based on long-column, long-duration drainage test data and available soil properties. The paper first shows that predictive models perform poorly for coarse-grained soils, but four descriptive models can correctly fit the test data. Second, it provides predictive equations for the air-entry value (AEV), residual suction ur, and residual water content θr as simple functions of the effective size d10 and void ratio e. Third, it provides new formulas that relate d10 and e to the parameters of descriptive models; thus, enabling them to become very simple to use predictive models for ed10 > 0.05 mm. The paper finally shows that small variations in d10 and e have more influence on the saturated permeability than on the WRC parameters.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".