Soil and Water Loss Rates in Oxisols Under No-tillage System in Western Paraná, Brazil
Why this work is in the frame
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Bibliographic record
Abstract
The objective of work was to quantify soil and water loss rates as a function of slope variation, correlating these rates with soybean yield. In addition to developing multiple linear regression models that associate water and soil loss rates in function of their physical attributes. The experiment was conducted in an Oxisols under a no-tillage system. The experiment was carried out in Cascavel, PR, Brazil. Four slopes (3.5%; 8.2%; 11.4% and 13.5%) were considered as treatments. The water and soil loss rates were monitored in the rainfall occurring during the crop development cycle. The water drained in each plot was collected in gutters made of polyvinyl chloride and stored in containers for the quantification of soil and water losses. The stepwise backward method was used to identify the variables that had a significant influence on water and soil losses. The unevenness of the terrain did not influence the soil and water loss rates. The maximum soil and water losses during the soybean cycle were, respectively, 0.01962 Mg ha-1 and 4.07 m3 ha-1. The maximum soil and water losses occurred when the precipitation volume was up to 82 mm. Soil and water losses showed a higher correlation with macroporosity and bulk density. Soybean grain yield showed a higher linear correlation with water, and soil loss and was higher at the slopes of 8.2% and 13.4%. The low water and soil losses demonstrate the soil capacity, managed under a no-tillage system, to minimize environmental impacts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it