Molecular Breeding for Enhanced Rice Yield: The Role of Key Yield-Related Genes
Bibliographic record
Abstract
Rice ( Oryza sativa ) is one of the important global food crops, and the increase in its yield is of great significance for ensuring food security and alleviating the food crisis. This study analyzes the research progress and application of molecular breeding in improving rice yield. By revealing the potential role of key yield related genes in improving rice yield and quality, molecular breeding technologies such as marker assisted selection (MAS), genome selection (GS), genetic engineering, and CRISPR/Cas9 are introduced. The principles, applications, and successful cases of these technologies in rice breeding are discussed. In addition, this study also delves into the functional characteristics, gene expression research, functional genomics methods, and strategies and challenges of integrating yield related genes into breeding plans. By summarizing the successful experience, lessons learned, and best practices of molecular breeding in improving rice yield, the aim is to provide valuable reference and inspiration for future rice breeding work, and promote innovation and development in rice breeding work.
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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) | 0.001 | 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 | 0.001 |
| 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".