Potential for wind erosion in alternative cropping systems
Bibliographic record
Abstract
The potential for wind erosion in agricultural soils is a function of the distribution of aggregates at the surface, soil structure and moisture, and crop residue. These properties were measured in a cropping systems study designed to determine the effect of input level and crop diversity on sustainability and the potential for wind erosion. The experiment was established on a sandy loam soil in the Dark Brown Soil Zone at Scott Saskatchewan. Input levels were organic, reduced and high, while cropping-diversity levels were low diversity, diverse annual and diverse annual perennial. Differences in residue were attributed to the effect of tillage and the relative levels of productivity in the systems. Spring and fall tillage in organic systems reduced the amount of residue compared to reduced input systems. Levels of crop residue were low at the beginning of this study, and crop residue cover should be measured in future years to determine potential for erosion. Crop residue levels may not reflect the system's potential for soil erosion until two rotation cycles are complete. Relative treatment differences observed in the study, were similar to those calculated with of the Douglas-Rickman decomposition equation and tillage coefficients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".