Bayesian estimation of soil-water characteristic curves
Bibliographic record
Abstract
The soil-water characteristic curve (SWCC) is a significant prerequisite for studying the mechanical properties of unsaturated soil. As experimental measurement of the SWCC is time-consuming, empirical methods have been suggested to estimate the SWCC. However, the uncertainty associated with SWCC can be substantial. In this paper, a hybrid method based on Bayes’ theorem is suggested to estimate the SWCC, where an empirical method can be used to provide prior knowledge about the SWCC, and a limited quantity of measured data are used to update the SWCC. The Bayesian model is then solved with a Markov Chain Monte Carlo simulation. Through the suggested method, the valuable information provided by the empirical method can be combined with the measurement data. The suggested method can not only provide the best estimate about the SWCC, but also account for the associated uncertainty. Also, the effect of more measured points on the estimation of SWCC can be quantified. The suggested method provides a practical means to estimate the SWCC using a limited amount of data.
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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) | 0.001 | 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 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".