Prediction and modeling of permeability function and its application to the evaluation of breakthrough suction of a two-layer capillary barrier
Bibliographic record
Abstract
Different empirical formulas have been proposed to describe the water retention curve (WRC) and relative permeability (kr) of soils. This paper presents a Bayesian framework that evaluates not only the most probable empirical fitting constants, but also their joint probability density function. A dataset containing two soil classes — sand and silty loam — compiled from the UNSODA database is used for illustration. First, model constants of the van Genuchten’s WRC formula are calibrated and subsequently used to predict kr of the studied soils using two existing formulas based on Mualem’s and Burdine’s models. The best estimated kr in both formulas is found to skew towards the lower side of the measurement. Then, a new three-parameter empirical formula is proposed to describe kr with suction while the model constants are calibrated from the permeability data. Using the proposed framework, the statistical distribution of kr and subsequently the unsaturated permeability (kunsat), as a function of suction, can be obtained. The results are then applied to a hypothetical two-layer capillary barrier composed of soils of the compiled dataset to determine the breakthrough suction (ψBT) of the barrier. The proposed Bayesian approach gives a probabilistic distribution of ψBT instead of a single value in the traditional deterministic method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".