Fine Mapping of a Major QTL for Stay-Green Trait in Maize Using Near-Isogenic Lines
Bibliographic record
Abstract
The greenness retention trait is one of the key agronomic traits determining the stress resistance and high-yield potential of maize ( Zea mays L.), especially showing significant value under stress conditions such as drought and late maturity. In this study, Near-Isogenic Lines (NILs) were used as research materials to precisely locate a major QTL in corn that was significantly associated with greenness holding traits, reveal its genetic basis and explore candidate gene resources. Through the construction of a high-density molecular marker map and precise phenotypic evaluation, The fine localization region of this QTL on chromosome 9 was identified under multi-environment experiments. Further, through recombinant single-plant analysis, candidate gene expression profiling and functional annotation, multiple key genes that may be involved in regulating chlorophyll degradation, photosynthesis maintenance and antioxidant pathways were preliminarily identified. This study also verified the expression and explored the biological significance of typical candidate genes such as Stay-Green1 ( SGR1 ) and SAG12 , providing an important basis for the molecular breeding of green-holding traits in maize and potential targets for the improvement of complex traits such as drought resistance and delayed senescence.
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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".