Acquisition of desiccation tolerance during seed development is associated with oxidative processes in rice
Bibliographic record
Abstract
Desiccation tolerance is an important characteristic of rice seed quality during seed drying. In this study, two varieties of Oryza sativa L. subsp. japonica (Jiucaiqing and Wuyunjing 7) were used to investigate the dynamic changes in rice seeds that are associated with desiccation tolerance (germination traits produced by dried seeds), grain mass (GW), moisture content (MC), and electrical conductivity (EC) during seed development (18 to 42 days after heading (DAH)). Changes in the levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2) in fresh, dry, and imbibed states, and antioxidant enzyme activity (superoxide dismutase (SOD); catalase (CAT); and glutathione reductase (GR)) in fresh and imbibed seeds were also evaluated during seed development. The results showed that seeds started to acquire desiccation tolerance at 26 DAH and reached the highest levels at 38 DAH. The increase in GW and decline in MC coincided with the acquisition of desiccation tolerance. MDA and H2O2 levels, and SOD, CAT, and GR activities in fresh and imbibed seeds rapidly decreased during the early developmental stages (18–30 DAH), and then their levels gradually stabilized (30–42 DAH). Regression analyses indicated that H2O2 levels in fresh seeds was negatively correlated with seed germination, whereas SOD activity in imbibed seeds was positively correlated with seed germination. H2O2 and SOD are, therefore, good parameters for assessing desiccation tolerance during rice seed development.
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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".