Estimation of water retention curve of granular soils from particle-size distribution — a Bayesian probabilistic approach
Bibliographic record
Abstract
This study proposes two empirical relationships to estimate the parameters of van Genuchten’s formula for modeling the water retention curve from the particle-size distribution. The relationships are determined by the Bayesian probabilistic method for selecting the most plausible class of models based on a database of 90 soil samples. The highest plausibility model among the selected relationships shows that the parameter α can be expressed as a first-order function of the particle size at 50% passing (d 50 ) and parameter n is expressed as a third-order polynomial of the reciprocal of the standard deviation of geometric mean particle size (σ g ). The predictability of proposed relationships for other soils outside the calibrated database is also presented. It is found that the model prediction is highly consistent with the measurements for sands. However it only matches well with the measurements in the low suction regime for soils with at least 20% of fines content.
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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 | 0.000 |
| 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 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".