Effect of Cover Crops Associated With Lettuce Production Under No-Tillage System
Bibliographic record
Abstract
The use of cover crops has benefits for the chemical, physical and biological properties of the soil. However, together with the need for good vegetable productivity, considerable challenges arise in several regions of Brazil. The preparation of conventional soil for initiating no-tillage systems is necessary to create a management history and assimilate the benefits of the no-tillage system in vegetables, ensuring sustainable production. The objective of this research was to evaluate lettuce yield as a function of different cover crops as a function of resistance to soil penetration. The experiment was conducted in the horticulture sector of the University of Rio Verde, Rio Verde, Goiás, Brazil. The cover crops used were sunflower (Helianthus annuus), sunn hemp (Crotalaria juncea), and fallow, and the vegetable used was lettuce (Lactuca sativa). The variables analyzed were plant height and lettuce yield, straw decomposition, and soil resistance to penetration. The collected data were submitted to analysis of variance, and if significant, was compared by Tukey test (p < 0.05) and regression analysis. The lettuce height in the different management systems showed no statistical differences. The decomposition of the straw presented accentuated degradation for the evaluated cover crops and the productivity was bigger in the straw of crotalaria and fallow. Soil resistance for cover crops was not greater than 2 MPa.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".