A single seed treatment mediated through reactive oxygen species increases germination, growth performance, and abiotic stress tolerance in<i>Arabidopsis</i>and rice
Bibliographic record
Abstract
Abstract Hydroxyl radical (•OH) is considered to be the most damaging among reactive oxygen species. Although afew studies have reported on its effects on growth and stress adaptation of plants, no detailed studies have been performed using •OH in germination and early seedling growth under abiotic stresses. Here we report a single seed treatment with •OH on germination and seedling growth of Arabidopsis and rice under non-stressed (ambient) and various abiotic-stressed conditions (chilling, high temperature, heat, and salinity). The treatment resulted in faster seed germination and early seedling growth under non-stressed conditions, and, interestingly, these effects were more prominent under abiotic stresses. In addition, Arabidopsis seedlings from treated seeds showed faster root growth and developed more lateral roots. These results show apositive and potential practical use for •OH in model and crop plants for direct seeding in the field, as well as improvement of tolerance against emerging stresses. Abbreviations: AUC: area under curve; MGT: mean germination time; t50: time to reach 50% germination; U7525: time for uniform germination from 25% to 75%; ROS: reactive oxygen species; GSI: germination speed index; SI: stress index; DI: dormancy index
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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 | 0.001 |
| 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".