Multifractal Characterization of Soil Pore Systems
Bibliographic record
Abstract
Spatial arrangement of soil pores determines soil structure and is important to model soil processes. Geometric properties of individual pores can be estimated from thin sections, but there is no satisfactory method to quantify the complexity of their spatial arrangement. The objective of this work was to apply a multifractal technique to quantify properties of ten contrasting soil pore systems. Binary images (500 by 750 pixels, 74.2 μm pixel −1 ) were obtained from thin sections and analyzed to obtain f (α) spectra. Pore area and pore perimeter were measured from each image and used to estimate a shape factor for pores with area larger than 0.27 × 10 6 μm 2 Mean area of the lower (MA L ) and upper (MA U ) one‐half of cumulative pore area distributions were calculated. Pore structures with large (MA U > 10 × 10 6 μm 2 ) and elongated pores exhibited “flat” f (α)‐spectra typical of homogenous systems (three soils). Massive type structure with small (MA U < 1 × 10 6 μm 2 ) rounded and irregular pores resulted in asymmetric f (α)‐spectra (two soils). Well defined and symmetric f (α)‐spectra were obtained with soil structures having elongated pores of intermediate size (1 × 10 6 < MA U < 10 × 10 6 μm 2 ) clustered around relatively small structural units (five soils). Multifractal parameters defining the maximum of the f (α)‐spectra were correlated to total porosity ( P < 0.001), and silt content ( P < 0.05). This study demonstrates that the spatial arrangement of contrasting soil structures can be quantified and separated by the properties of their f (α)‐spectra. Multifractal parameters quantifying spatial arrangement of soil pores could be used to improve classifications of soil structure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".