Comparisons of Appearance and Eating Quality Traits Between Early and Late Maturing Soft Rice
Bibliographic record
Abstract
Soft Japonica rice is popular in Yangtze River Delta of China in recent years because of its high eating quality. In this study, high eating quality soft Japonica rice with early and late maturity was used to analyze their eating quality traits. The appearance, texture, and physicochemical characteristics, gelatinization and pasting properties, and fine structures between early and late maturing soft rice were compared. Early maturing soft rice had higher whiteness, chalkiness, gelatinization temperature, peak viscosity, breakdown viscosity, long branch-chain amylopectin, amylopectin chains (DP > 25), and molecular weight than late maturing soft rice. The higher gelatinization and pasting properties in early maturing soft rice probably be attributed to its higher amount of long branch-chain amylopectin, proportions of amylopectin chains (DP > 25) and molecular weight, compared with late maturing soft rice. Late maturing soft rice had higher amylose content, gel consistency, short branch-chain amylopectin, amylopectin short chains (DP 6–12), setback viscosity, and consistence viscosity than early maturing soft rice. The appearance and eating quality of late maturing soft rice were significantly higher than those of early maturing soft rice.
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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 | 0.000 | 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 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".