Identification, Genetic Analysis and Fine Mapping of Early Senescence Mutant <i>esh</i> in Rice
Bibliographic record
Abstract
The early senescence of rice during the reproductive growth period seriously affects rice yield and quality. In this study, ethyl methylsulfonate (EMS) was used to induce the japonica rice variety Hwacheongbyeo to obtain a leaf early senescence mutant during the reproductive growth period of rice, and named as es-h ( early senescence - Hwacheongbyeo ). Phenotypic analysis showed that the mutant began to have rust spots on the leaves after heading, and withered rapidly with the filling process, the whole plant died off by the fifth week of heading. Agronomic trait analysis showed that, compared with the wild type, the es - h mutant had no significant changes in heading date, panicle length, panicle excertion and panicle number, while significantly reduced in plant height, grain number per panicle, seed setting rate and thousand grain weight. Physiological analysis showed that after heading of es - h mutant, the SPAD value, chlorophyll content, Fv/Fm value and soluble protein content of its flag leaf all decreased sharply. Genetic analysis revealed that the premature aging traits of es - h mutants were controlled by recessive single gene. The target gene was located on the 44.2 kb physical segment of the long arm of chromosome 1 through gene mapping. This study provides a basis for Es-h gene cloning and functional analysis, and molecular mechanism of premature aging.
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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".