Long-term tillage and crop rotation effect on soil aggregation
Bibliographic record
Abstract
Tillage and cropping sequences play a key role in controlling soil aggregation. We measured water-stable aggregate (WSA), wind erodible fraction (WEF), and geometric mean diameter (GMD) for six mid to longterm (8 to 25 years) experiments comparing tillage and cropping sequences in the Brown, Dark Brown, and Black Chernozemic soils of Saskatchewan. In the coarse-textured soil, no-tillage (NT) had a higher value of WSA by 49% more than in the wheat-phase of fallow-wheat (F-W), and had a lower value of WEF by 27% less than in the fallow-phase of F-W compared with minimum tillage (MT). In the medium-textured soils, NT had a higher WAS, ranged from 17 to 38%, and a lower WEF, ranged from 37 to 64% compared with conventional tillage (CT), depending on crop rotation systems. The reduced WEF under NT in the medium-textured soils was due mainly to increased GMD. In the fine-textured soils, NT had a higher WSA, ranged from 10 to 19% compared with MT or CT, and a lower WEF by 47% compared with MT only in the heavy clay soil. Change in GMD was not detectable in the light- and fine-textured soils. Continuous cropping compared with rotations containing fallow improved soil physical properties by increasing WSA, reducing WEF in the medium and fine-textured soils, and increasing GMD only in the medium-textured soils. Of the three soil physical properties determined in this study, WSA was the most sensitive to changes in tillage and crop rotations, then WEF and the least GMD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".