Wavelet‐based multifractal analysis of field scale variability in soil water retention
Bibliographic record
Abstract
Better understanding of spatial variability of soil hydraulic parameters and their relationships to other soil properties is essential to scale‐up measured hydraulic parameters and to improve the predictive capacity of pedotransfer functions. The objective of this study was to characterize scaling properties and the persistency of water retention parameters and soil physical properties. Soil texture, bulk density, organic carbon content, and the parameters of the van Genuchten water retention function were determined on 128 soil cores from a 384‐m transect with a sandy loam soil, located at Smeaton, SK, Canada. The wavelet transform modulus maxima, or WTMM, technique was used in the multifractal analysis. Results indicate that the fitted water retention parameters had higher small‐scale variability and lower persistency than the measured soil physical properties. Of the three distinct scaling ranges identified, the middle region (8–128 m) had a multifractal‐type scaling. The generalized Hurst exponent indicated that the measured soil properties were more persistent than the fitted soil hydraulic parameters. The relationships observed here imply that soil physical properties are better predictors of water retention values at larger spatial scales than at smaller scales.
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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 | 0.001 | 0.001 |
| 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".