X‐ray Computed Microtomography for the Study of the Soil–Root Relationship in Grassland Soils
Bibliographic record
Abstract
The exploration of soil structure and its consequences for ecological functions is of prime importance for understanding of the “critical zone.” In soils, the relationship between soil structure and plant roots—which influences water dynamics in soil—deserves special interest. With X‐ray computed microtomography (micro‐CT), soil structure and roots may be visualized and quantified simultaneously. We analyzed undisturbed soil samples from three grassland sites. After scanning the soil cores with an X‐ray micro‐CT scanner (resolution 40 μm), roots were delineated from the soil material and soil pore space by means of their X‐ray grey value characteristics followed by the use of space transformation during image analyses. To determine efficacy of the X‐ray micro‐CT to identify roots, root volumes and surfaces were quantified with a standard root washing method showing good correspondence. A strong positive correlation between root volume and surfaces and the solid surface/solid volume ratio was found, with greater root growth in a more aggregated and porous soil. Preliminary analyses suggest that the relationship between soil structure and root patterns is related to the intensity of land management at the grassland sites, with reduced root volumes and surfaces at sites with increased land use intensity. This methodology has substantial potential for further research on management influences on soil structure and root growth across landscapes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".