Nitrogen loss by surface runoff from different cropping systems
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Bibliographic record
Abstract
Reducing nitrogen (N) loss from agricultural soils as surface runoff is essential to prevent surface water contamination. The objective of 3-year study, 2007–09, was to evaluate surface runoff and N loss from different cropping systems. There were four treatments, including one single-crop cropping system with winter wheat (Triticum aestivum L.) followed by summer fallow (wheat/fallow), and three double-cropping systems: winter wheat/corn (Zea mays L.), wheat/cotton (Gossypium hirsutum L.), and wheat/soybean (Glycine max L. Merrill). The wheat/fallow received no fertiliser in the summer fallow period. The four cropping systems were randomly assigned to 12 plots of 5 m by 2 m on a silty clay soil. Lower runoff was found in the three double-cropping systems than the wheat/fallow, with the lowest runoff from the wheat/soybean. The three double-cropping systems also substantially reduced losses of ammonium-N (NH4+-N), nitrate-N (NO3–-N), dissolved N (DN), and total N (TN) compared with the wheat/fallow. Among the three double-cropping systems, the highest losses of NO3–-N, DN, and TN were from the wheat/cotton, and the lowest losses were from the wheat/soybean. However, the wheat/soybean increased NO3–-N and DN concentrations compared with wheat/fallow. The losses in peak events accounted for >64% for NH4+-N, 58% for NO3–-N, and 41% for DN of the total losses occurring during the 3-year experimental period, suggesting that peak N-loss events should be focussed on for the control of N loss as surface runoff from agricultural fields.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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