Capillary pressure-dependent anisotropy of layered unsaturated soils
Bibliographic record
Abstract
This paper presents an approach based on a conceptualization of combining the neural network based pedo-transfer function (PTF) results with the thin layer concept to explore capillary-pressure-dependent anisotropy in relation to soil texture and soil bulk density. The effects of capillary pressure (or saturation degree) on the hydraulic conductivity anisotropy of unsaturated soils are still poorly understood. The main objective is to examine how anisotropy characteristics are related to the relationships between hydraulic parameters and the basic soil attributes such as texture and bulk density. The hydraulic parameters are correlated with the texture and bulk density based on the pedo-transfer function (PTF) results. It is demonstrated that non-monotonic behavior of the unsaturated soil anisotropy in relation to the capillary pressure is only observed when the saturated hydraulic conductivity and the shape parameter are both related to the particle diameter. Therefore, it is suggested that this behavior is mainly due to the coupled dependence of the layer saturated hydraulic conductivities and the shape factors on the texture and bulk density. The results illustrate that the inter-relationships of soil texture, bulk density, and hydraulic properties may produce vastly different characteristics of anisotropic unsaturated soils.Key words: Anisotropy, unsaturated soils, capillary pressure-dependent
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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 | 0.000 | 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 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".