Research in wheat heat tolerance breeding
Bibliographic record
Abstract
With the global warming, heat stress has become one of the primary factors limiting wheat yield and quality. Developing heat-tolerant wheat varieties is crucial for stabilizing wheat production and adapting to climate change. This study summarizes current progress in wheat heat tolerance breeding, analyzing the effectiveness of traditional and modern breeding technologies such as marker-assisted selection (MAS), genomic selection (GS), and gene editing. It also explores the role of omics technologies in improving wheat heat tolerance, in-tegrating case analyses, and proposing future directions to address challenges in wheat heat tolerance breed-ing. The research indicates that the complexity of heat tolerance traits in wheat and their polygenic regulatory nature make it difficult for traditional breeding methods alone to effectively counteract the effects of heat stress. MAS and GS have significantly enhanced breeding efficiency, while gene editing technology provides a new pathway for precise improvement of heat tolerance genes. Additionally, the integrated application of transcriptomics, metabolomics, and proteomics has facilitated a deep understanding of heat tolerance mecha-nisms in wheat, promoting the identification and precise selection of candidate genes. This study provides a systematic reference for wheat heat tolerance breeding, revealing the potential of multi-technology integra-tion in improving heat tolerance and accelerating the development of wheat varieties that can adapt to climate change, thereby offering crucial support for global food security.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".