Long-Term Effects of Rice Cultivation on Soil Organic Nitrogen Dynamics
Bibliographic record
Abstract
This study aims to investigate the long-term effects of rice cultivation on soil organic nitrogen dynamics. Rice is an important food crop worldwide. Its cultivation has a significant impact on soil ecosystems, especially on the dynamic changes of soil organic nitrogen. By reviewing existing literature, the changes in soil organic nitrogen content under different planting years and management measures were analyzed to reveal the effects of rice cultivation on soil health. This study found that long-term rice cultivation may lead to fluctuations in soil organic nitrogen content and affect the physical and chemical properties of the soil. Seasonal and interannual changes, agricultural management practices (such as fertilization and crop rotation) and other factors play an important role in the dynamic changes of organic nitrogen. This study provides new insights into the understanding of the soil nitrogen cycle mechanism and proposes a scientific basis for sustainable agricultural management. Through reasonable agricultural management measures, such as the use of organic fertilizers and green manures and the optimization of flooding management in rice fields, soil health can be improved, the stability and availability of soil organic nitrogen can be enhanced, and rice yield and ecological environment protection can be promoted.
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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 | 0.000 |
| 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) | 0.001 | 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".