Effects of elevated nitrogen fertilizer on the multi‐level structure and thermal properties of rice starch granules and their relationship with chalkiness traits
Bibliographic record
Abstract
Abstract BACKGROUND Chalkiness in rice reduces its market value and affects consumer acceptance. Research on the mechanism of chalkiness formation has focused primarily on the activity of key enzymes of carbon metabolism and starch accumulation. The relationship between the formation of chalkiness induced by N fertilizer and rice starch's multi‐level structure and thermal properties still needs to be fully elucidated. RESULTS In this study, the rates of chalky grains and degree of chalkiness decreased with the increase in N fertilizer dosage. This was attributed to an increased proportion of short chains, ordered structure carbon chains, small starch granules, and branched starches, and a higher degree of crystallinity and Δ H g in protein, and a decreased proportion of amylose, large starch granules, and weighted average diameter of starch granule surface area and volume. Application of N fertilizer promoted an increased proportion of short‐branched chain amylopectin to develop a more ordered carbohydrate structure and crystalline lamella. These effects enhanced the normal development and compactness of starch granules in grains, and improved their arrangement morphology, thereby reducing the chalkiness in rice. CONCLUSION These changes in starch multi‐level structure and protein improve the physicochemical characteristics of starch and enhance the fullness, crystallinity and compactness of starch granules, while synergistically increasing the regularity and homogeneity of starch granules and thus optimizing the stacking pattern of starch granules, leading to a reduction in rice chalkiness under nitrogen fertilization and thus improving the appearance of rice. © 2023 Society of Chemical Industry.
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.
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 | 0.001 |
| 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".