Study on the Chalkiness Character of Giant Embryo Rice and Observation on Its Microstructure in Grains
Bibliographic record
Abstract
Grain chalkiness greatly affects the grain appearance and milling, eating, cooking, and nutritional qualities, thus it is one of the most important traits of grain qualities. Based on the study of chalkiness characters in endosperm of different giant embryo rice, the relationship between the differences of chalkiness characters in endosperm of different giant embryo rice and the morphological structure, arrangement and development of starch granules in endosperm was clarified, which will lay a solid foundation for further revealing the formation mechanism of chalkiness characters in giant embryo rice and cultivating excellent new varieties of giant embryo rice. In this study, the chalkiness of endosperm (including chalkiness rate, chalkiness degree and chalkiness area) of 16 different rice varieties were investigated and analyzed. The results showed that the chalkiness of endosperm was closely related to the morphological structure and arrangement of starch granules in endosperm cells of different kinds of giant embryo rice, and there were significant differences between the chalkiness of different parts of the same giant embryo rice and the morphological structure and arrangement of starch granules in endosperm cells. Therefore, the results of this study will provide important information for the improvement of quality characters and the breeding of new rice varieties.
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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 | 0.001 | 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".