Scaling of pores in 3D images of Latosols (Oxisols) with contrasting mineralogy under a conservation management system
Bibliographic record
Abstract
The aim of this study was to evaluate the spatial and morphological configuration of the pore space in 3D images of Latosols with different mineralogy under a conservation tillage system in a coffee crop area. The visualisation and quantification of pore size distribution by data mining and spatial variability by semi-variogams were investigated in 3D images with 60-µm spatial resolution generated by X-ray CT scan (EVS/GE MS8x-130) in soil core samples collected at different depths of a kaolinitic Red-Yellow Latosol (RYL) and a gibbsitic Red Latosol (RL) from Brazil. Greater spatial variability occurred in the horizontal direction of the 3D image, a novel finding in this area of research. The pores detected were different between the Latosols studied, mainly at 0.20–0.34 m depth. The largest number (>4000) and volume (±30 mm3) of pores was found in the RL. The soil classes differed in 3D pore characteristics, and this aspect may be important in the characterisation of causes of pore variability. Sphericity was similar for both soils, with greater emphasis on pore classes with a diameter <0.4 mm, mainly at the 0.20–0.34 m depth. A higher percentage of spheroid pores occurred in RL (±25%), whereas the platy pores were more abundant in RYL (>15%).
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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 | 0.001 | 0.000 |
| 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".