Metabolite accumulation contributes to differences in seed germination of water-saving and drought-resistance rice under dry direct seeding
Bibliographic record
Abstract
Dry direct seeding of rice has emerged as an effective method for reducing the excessive water demand associated with conventional rice transplantation, presenting significant potential for enhancing sustainability. However, this cultivation method is hindered by high seed usage and often inconsistent and low seedling emergence. Seed priming, a pre-sowing treatment, has been employed to mitigate these issues, but the inconsistent effects of exogenous priming agents remain a concern. Currently, there is limited molecular-level information on the uneven seedling emergence and effective screening methods for priming agents. In this study, we employed a metabolomics approach using advanced chromatography and mass spectrometry technology to identify differential accumulation of metabolites (DAMs) in seeds with varying germination energies. The seed priming technique was also used to validate the identified DAMs. We investigated the proportion of different specific gravity seeds and the corresponding germination energy across 20 varieties and established a relationship between different specific gravity seeds and germination energy. Our results showed that seeds with high and low germination energy differed in several metabolites, including amino acids, organic acids, and others. We further confirmed the critical role of these DAMs in determining seed germination energy under dry direct seeding. This research provides valuable insights into the metabolic mechanisms associated with germination energy and offers a useful approach for screening effective endogenous seed priming agents.
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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) | 0.001 | 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".