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
Improving rice grain quality is a key objective in modern agriculture, aiming to meet the diverse consumer demands for nutrition, appearance, and palatability. This study reviews the effects of Integrated Nutrient Management (INM) on major rice quality traits, including milling quality, appearance, eating quality, and nutritional composition. A field trial was conducted in a representative rice-producing area of Jiangsu Province, China, to evaluate the potential of INM strategies in enhancing grain quality. The experiment combined organic fertilizers, controlled-release fertilizers, and scientifically timed fertilization schedules to achieve precise nutrient regulation. Results showed that INM significantly improved the head rice recovery rate, reduced grain chalkiness, and promoted the accumulation of protein, zinc, and iron. Additionally, eating quality indicators such as gel consistency and taste scores were enhanced, while rice yield remained stable. This study highlights the importance of integrating precision fertilization with soil health management and aims to provide a practical foundation for the sustainable production of high-quality rice.
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 | 0.000 | 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) | 0.000 | 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