Study on the Physiological Basis of Efficient Nitrogen Utilization and Green Fertilization Strategy of Rice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the context of the current green transformation of agriculture and sustainable ecological development, improving the nitrogen use efficiency of rice has become an important issue to ensure food security and reduce environmental pollution. This study systematically explored the key physiological mechanisms of rice in the process of nitrogen absorption, transport and metabolism, focusing on the expression and regulatory role of key genes such as OsNRT1.1B , OsAMT1.2 , and OsGS1;1 , as well as the functions of transcription factors such as NLP, DOF, and MYB in the regulation of nitrogen metabolism. Combined with the development trend of green agriculture in recent years, this study further evaluated the practical effects of the "one base and one topdressing" fertilization mode of controlled-release fertilizers and the integrated management with green control technology, and analyzed the synergistic effect of high-efficiency varieties and green fertilization modes through a typical case of the demonstration field in Dahao Village, Jiashan. The study showed that indica-japonica hybrid rice and excellent late japonica rice varieties have a strong responsiveness to nitrogen supply, and can significantly improve nitrogen use efficiency and yield stability under reasonable cultivation management and precision fertilization. This study not only provides theoretical support and practical path for reducing nitrogen fertilizer and increasing its efficiency, but also provides a reference for the construction of regional rice ecological planting system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it