Scaling analysis of soil water retention parameters and physical properties of a Chinese agricultural soil
Bibliographic record
Abstract
Measurement scale of soil water retention parameters is often different from the application scale. Knowledge of scaling property of soil hydraulic parameters is important because scaling allows information to be transferred from one scale to another. The objective of this study is to examine whether these parameters have fractal scaling properties in a cultivated agricultural soil in China. Undisturbed soil samples (128) were collected from a 640-m transect at Fuxin, China. Soil water retention curve and soil physical properties were measured from each sample, and residual water content (θr), saturated soil water content (θs), and parameters αvG and n of the van Genuchten water retention function were determined by curve-fitting. In addition, multiple scale variability was evaluated through multifractal analyses. Mass probability distribution of all properties was related to the support scale in a power law manner. Some properties such as sand content, silt content, θs, and n had mono-fractal scaling behaviour, indicating that, whether for high or low data values, they can be upscaled from small-scale measurements to large-scale applications using the measured data. The spatial distribution of organic carbon content had typically multifractal scaling property, and other properties – clay content, θr, and αvG – showed a weakly multifractal distribution. The upscaling or downscaling of multifractal distribution was more complex than that of monofractal distribution. It also suggested that distinguishing mono-fractals and multifractals is important for understanding the underlying processes, for simulation and for spatial interpolation of soil water retention characteristics and physical properties.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".