A review of fractal, prefractal and pore-solid-fractal models for parameterizing the soil water retention curve
Bibliographic record
Abstract
Ghanbarian-Alavijeh, B., Millán, H. and Huang, G. 2011. A review of fractal, prefractal and pore-solid-fractal models for parameterizing the soil water retention curve. Can. J. Soil Sci. 91: 1–14. The soil water retention curve is an important hydraulic parameter for characterizing water flow and contaminant transport in porous media. Therefore, many empirical, semi physical, and physical models of the soil water retention curve have been proposed. Among them, fractal models appear to be a useful approach for modeling soil as a heterogeneous porous medium and its hydraulic characteristics. Fractal models are mathematically based, and their parameters have physical meanings. In this study, we review published fractal, prefractal and pore-solid-fractal models for soil water retention curves including Tyler and Wheatcraft, Rieu and Sposito, Perrier et al., Perfect, Bird et al., Millán and González-Posada, and Cihan et al. models. In the pore-solid fractal (PSF) approach the pore phase and matrix phase have a finite volume even for an infinite number of iterations. The results of fitting the PSF model to measured soil water retention data indicate that this model works well, particularly at lower water contents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".