Water, Soil and Nutrients Losses by Runoff at Hillslope Scale in Agricultural and Pasture Production in Southern Brazil
Bibliographic record
Abstract
Agricultural activity, if not well managed, is an important source of water pollution mainly by surface runoff. The aim of this study was to evaluate losses of water, soil and soluble nutrient (phosphorus, nitrogen and carbon) via runoff in large plots (hillslope from 3,000 to 11,000 m2) at slope of 4 to 5% in annual crops (corn and sunflower) and pasture systems under no-tillage and no-pesticides. The study was carried out at the Canguiri Experimental Farm of the Federal University of Paraná, Southern Brazil, in soil classified as Ferralsol in three systems (crop, pasture and crop-pasture). Soil physical and chemical attributes as well as topographic indices and soil cover were investigated to evaluate the possible impacts of these variables on losses. The runoff was measured after each rainfall event from November 2014 to October 2015. Runoff samples were taken to analyse sediment and nutrients. All systems had low water, soil and nutrients losses compared to no conservative agricultural systems. Higher losses occurred in the annual crops system and were influenced by soil cover, with an annual runoff coefficient of 0.62% (7 mm of water loss) and an annual soil loss of 2.6 kg ha-1. The seasonality (winter/summer) did not influence soil, water and nutrient losses.
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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 | 0.000 | 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) | 0.000 | 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 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".