<sup>15</sup>N Tracer-based Analysis on Nitrogen Utilization Laws of Rice Under Different Irrigation Modes
Bibliographic record
Abstract
In order to promote the efficient and coordinated utilization of water and nitrogen in rice, the 15 N missing technology experiment was adopted to study the effects of nitrogen application rate (N 0 , N 160 , N 200 , N 240 ) and irrigation mode (Wet-dry alternation, conventional irrigation) on nitrogen absorption, transport, residue, loss and nitrogen utilization of super rice. The results showed that with the increase of nitrogen application rate, the accumulation of fertilizer nitrogen and total nitrogen in rice plants at different growth stages, and the accumulation of nitrogen in various organs at mature stage increased significantly, and the accumulation of nitrogen per panicle at mature stage was the highest, reaching 67.20%~69.02%. The transport amount of nitrogen from different sources in different vegetative organs of rice plants increased with the increase of nitrogen application rate, and the comparison of nitrogen transport amount in organs was as follows: leaf > stem > root; After full heading, the accumulation of nitrogen from different sources and its contribution rate to grain nitrogen per spike increased with the increase of nitrogen application rate, but with the increase of nitrogen application rate, the residual amount of soil fertilizer nitrogen increased significantly, the residual rate showed a downward trend, and the nitrogen loss rate increased significantly. Dry-wet alternate irrigation treatment can increase nitrogen accumulation in different organs of rice in different periods, improve nitrogen use efficiency and rice yield to a certain extent. This study summarized the laws of nitrogen absorption, utilization, residue and loss of rice under different irrigation conditions and different nitrogen application conditions, which provided a theoretical basis for efficient cultivation of rice.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".