Short-term effects of tillage practices on soil aggregate fractions in a Chinese Mollisol
Bibliographic record
Abstract
Soil aggregate-size distribution and soil aggregate stability are used to characterize soil structure. Quantifying the changes of structural stability of soil is an important element in assessing soil and crop management practices. A 5-year tillage experiment consisting of no till (NT), moldboard plow (MP) and ridge tillage (RT), was used to study soil water-stable aggregate size distribution, aggregate stability and aggregate-associated soil organic carbon (SOC) at four soil depths (0–5, 5–10, 10–20 and 20–30 cm) of a clay loam soil in northeast China. Nonlinear fractal dimension (Dm) was used to characterize soil aggregate stability. No tillage led to a significantly greater aggregation for >1 mm aggregate and significant SOC changes in this fraction at 0–5 cm depth. There were significant positive relationships between SOC and >1 mm aggregate, SOC in each aggregate fraction, but there was no relationship between soil aggregate parameters (the proportion of soil aggregates, aggregate-associated SOC and soil stability) and soil bulk density. After 5 years, there was no difference in Dm of soil aggregate size distribution among tillage treatments, which suggested that Dm could not be used as an indicator to assess short-term effects of tillage practices on soil aggregation. In the short term, > 1 mm soil aggregate was a better indicator to characterize the impacts of tillage practices on quality of a Chinese Mollisol, particularly in the near-surface layer of the soil.
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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 | 0.000 | 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".