Gibberellin biosynthesis and signal transduction is essential for internode elongation in deepwater rice
Bibliographic record
Abstract
Under flooded conditions, the leaves and internodes of deepwater rice can elongate above the water surface to capture oxygen and prevent drowning. Our previous studies showed that three major quantitative trait loci (QTL) regulate deepwater-dependent internode elongation in deepwater rice. In this study, we investigated the age-dependent internode elongation in deepwater rice. We also investigated the relationship between deepwater-dependent internode elongation and the phytohormone gibberellin (GA) by physiological and genetic approach using a QTL pyramiding line (NIL-1 + 3 + 12). Deepwater rice did not show internode elongation before the sixth leaf stage under deepwater condition. Additionally, deepwater-dependent internode elongation occurred on the sixth and seventh internodes during the sixth leaf stage. These results indicate that deepwater rice could not start internode elongation until the sixth leaf stage. Ultra-performance liquid chromatography tandem mass-spectrometry (UPLC-MS/MS) method for the phytohormone contents showed a deepwater-dependent GA1 and GA4 accumulation in deepwater rice. Additionally, a GA inhibitor abolished deepwater-dependent internode elongation in deepwater rice. On the contrary, GA feeding mimicked internode elongation under ordinary growth conditions. However, mutations in GA biosynthesis and signal transduction genes blocked deepwater-dependent internode elongation. These data suggested that GA biosynthesis and signal transduction are essential for deepwater-dependent internode elongation in deepwater 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".