Aggregate pore and shape properties were more strongly correlated to soil organic carbon in large aggregates: Evidence from a long-term management-induced soil carbon gradient
Bibliographic record
Abstract
The interplay between soil structure and soil organic carbon (SOC) is complex and affects key soil functions. There is limited knowledge on how this relationship changes with the size of the structural unit studied. The objective of this study was to quantify the pore and shape characteristics of soil aggregates of varying sizes, and their relationships with SOC under different soil management regimes. Soils were sampled in March 2015 from the Highfield Ley-Arable Long-Term Experiment at Rothamsted Research. This experiment includes bare fallow, continuous arable rotation, ley-arable rotation, and grass treatments. A total of 24 aggregates from each treatment and size class (2–4, 4–8, and 8–16 mm) were subjected to X-ray micro-CT scanning at 40 μm voxel resolution. Results showed that permanent grass not only increased SOC accumulation, but also promoted pore connectivity of soil aggregates compared to bare fallow, regardless of aggregate size. Additionally, the pore and shape characteristics of larger aggregates (4–8 and 8–16 mm) were more sensitive to soil management compared to smaller aggregates (2–4 mm). The relationships between SOC and aggregate structural characteristics were strong for both 8–16 and 4–8 mm aggregates but weak for 2–4 mm aggregates. Furthermore, the responses of pore connectivity and sphericity to SOC increased with aggregate size. The results suggest that organic matter input plays an essential role in shaping aggregate structural characteristics and aggregate rearrangement (especially in larger aggregates).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".