QTL analysis of internode elongation in response to gibberellin in deepwater rice
Bibliographic record
Abstract
Gibberellin (GA) is a plant hormone that has important roles in numerous plant developmental phases. Rice plants known as deepwater rice respond to flooding by elongating their internodes to avoid anoxia. Previous studies reported that GA is essential for internode elongation in deepwater rice. Quantitative trait locus (QTL) analyses identified QTLs regulating internode elongation in response to deepwater conditions. However, the interaction between internode elongation and regulators of GA sensitivity in deepwater rice is unknown. In this study, we applied GA to recombinant inbred lines of T65 (non-deepwater rice) and Bhadua (deepwater rice), and performed a QTL analysis of internode elongation in response to GA. GA-induced internode elongation was detected only in deepwater rice. Our QTL analysis revealed two major QTLs on chromosomes 3 and 9 regulating total internode length, lowest elongated internode and number of elongated internodes. Furthermore, the QTL on chromosome 3 acted as an enhancer of other QTLs (e.g. the QTL on chromosome 12). Nearly isogenic lines of deepwater rice carrying the QTL regions from chromosomes 3 and 12 of the deepwater rice C9285 showed internode elongation in response to GA. Thus, these QTLs may regulate GA responsiveness in deepwater rice. This study furthers our understanding of the mechanism of internode elongation in 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 | 0.001 |
| Bibliometrics | 0.001 | 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".