Application of X-ray computed tomography for the analysis of soil micromorphology
Bibliographic record
Abstract
As a tool for the three-dimensional analysis of intact soil cores, X-ray computed tomography permits the quantification of soil voids characteristics above the micropore scale. The technique extends beyond the two dimensional perspective that has been the limitation of traditional soil thin section analysis, and unlike traditional, destructive laboratory analysis, is inclusive of both the interconnected and isolated fractions of the voids network. It is possible to quantify the frequency, spatial distribution, size, shape, and orientation of voids within a sample core, all of which reflect the interactions of soil forming factors. One previously unexplored phase of the methodology employed during the X-ray CT analysis of soils, is the influence of the method used to set final voxel size on the analysis of the resulting image models. Final voxel size can be established at three different stages: image acquisition, volume model reconstruction, or post-reconstruction re-sampling, The total % of detectable voids, and the distribution of voids by volume, were evaluated in ten three-dimensional models of the same volume space within a sample aggregate, each of which representing a different method used to set final voxel size. The results indicate that different methods used to set final voxel size do influence the quantification of soil voids, suggesting that it might not be possible to compare studies that used the same voxel size, but different methods for setting it. Of particular interest in this study is the influence of tillage management regimes on soil structure (total % of detectable voids, volume of voids, and atomic density of the solid phase). It is understood that management influences soil structure, and by association, the voids network. What is not clear is if those influences are characteristic of specific management regimes, or if the differences are quantifiable. The above-mentioned structural characteristics were evaluated in samples of medium textured soil from the University of Guelph Elora Research Station, representing a range of established long-term tillage regimes. The results do illustrate differences between treatments, suggesting that the tillage regimes might impart some characteristic changes to the soil structure. However, given the limited number of samples collected for this study, it is not possible to conclude that any of the observations are in fact characteristic of the tillage regime, only that it is possible to differentiate between samples based on those measurements. It is suggested that a second, more comprehensive study be undertaken, which evaluates more spatially and temporally distributed samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".