Quantifying influence of tillage practices on soil aggregate microstructure using synchrotron‐based micro‐computed tomography
Bibliographic record
Abstract
Abstract Studying the internal structure of soil aggregates is an insightful way to improve the understanding of soil aggregation process. However, it is still not clear how the macroaggregate pore structure is influenced by the application of long‐term tillage practices. In this study, we aimed to determine the differences in macroaggregate pore structure among long‐term tillage practices using micro‐computed tomography (SR‐μCT). Soil samples were collected from no tillage (NT) and ridge tillage (RT) plots and surrounding fields under conventional tillage (CT) and uncultivated soils (CK). The results showed that the diameters of most pores were greater than 30 μm, but >100 μm pores had the greatest percentage of total macroaggregate porosities in all four soils, that is NT, RT, CT and CK. The CK soil had a higher aggregate porosity of <30 μm and 30–60 μm, as well as a lower porosity of >100 μm than the NT, RT and CT soils. Total porosities within soil macroaggregates in both CK and CT were significantly lower than those in RT and NT, but the porosities of 30–60 μm and 60–100 μm within soil macroaggregates in both CK and RT were no significance. There were no significant differences in pore parameters between CK and RT. The CK and RT had higher mean weight diameter (MWD) and aggregate‐associated SOC contents than CT. Therefore, the pore structure of RT was similar to CK with good soil structure. Overall, RT was more appropriate tillage system to protect soil aggregate structure in black soils of Northeast China.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".