Mass fractal dimension of soil macropores using computed tomography: from the box‐counting to the cube‐counting algorithm
Bibliographic record
Abstract
Summary Transport phenomena in porous media depend strongly on three‐dimensional pore structures. Macropore networks enable water and solute to move preferentially through the vadose zone. A complete representation of their geometry is important for understanding soil behaviour such as preferential flow. Once we know the geometrical, topological and scaling attributes of preferential flow paths, we can begin computer simulations of water movement in the soil. The box‐counting method is used in three dimensions (i.e. cube‐counting algorithm) to characterize the mass fractal dimension of macropore networks using X‐ray computed tomography (CT) matrices. We developed an algorithm to investigate the mass fractal dimension in three dimensions and to see how it compares with the co‐dimensions obtained using the box‐counting technique in two dimensions. For that purpose, macropore networks in four large undisturbed soil columns (850 mm × 77 mm diameter) were quantified and visualized, in both two and three dimensions, using X‐ray CT. We observed an increasing trend between the fractal dimension and macroporosity for the four columns. Moreover, similar natural logarithm functions were obtained for the four cores by a least squares fit through plots of mass fractal dimension against macroporosity.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".