Multifractal properties of porosity as calculated from computed tomography (CT) images of a sandy soil, in relation to soil gas diffusion and linked soil physical properties
Bibliographic record
Abstract
Relationships between soil porosity and diffusive gas flux are poorly understood, partly because of a difference in measurement scales between the two. The complexity of soil pore systems can be described by multifractal analysis at the microscopic scale, whereas relative soil gas diffusion coefficients ( D s / D o ) are usually evaluated at the core scale. The objectives of this study were to (i) define a quantitative ‘pseudo‐macroporosity gas ’ from high‐resolution X‐ray computed tomography (CT) scanning images and characterize it for 10 intact soil cores, (ii) analyse the frequency distribution of pseudo‐macropores gas in the columns with a multifractal approach and (iii) assess relationships between D s / D o measured at the core scale and multifractal parameters describing the pore system heterogeneity within a core. The shape and symmetry of the singularity spectra and the degree of curvilinearity of the Rényi spectra show that the multifractal behaviour of the pseudo‐macroporosity gas distribution for a given CT image thresholding varied among soil columns. Correlations found between D s / D o and some parameter estimates of the singularity spectrum suggest that the distribution of pseudo‐macropores gas , depending on the CT image thresholding, influenced D s / D o . In particular, a strong correlation between D s / D o and the entropy dimension (D q = 1 ) indicates that D s / D o was influenced by the degree of spatial heterogeneity of the pseudo‐macroporosity gas distribution. The correlation dimension (D q = 2 ) was also linked to D s / D o , suggesting that a second‐order power law might describe the scaling relationship between pseudo‐macroporosity gas distribution and D s / D o . In conclusion, the multifractal description of soil porosity as calculated from CT images may be regarded as a way to improve our understanding of gas movement in soils at the core scale.
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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 | 0.003 |
| 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 | 0.000 |
| 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".