Bibliographic record
Abstract
Genome wide association studies (GWAS), as a powerful genomic tool, have been widely used to analyze the genetic basis of drought resistance traits in soybean. By mining quantitative trait loci (QTLs) related to drought resistance, they provide important molecular markers for drought resistance breeding. This study introduces the application of GWAS in the research of drought resistance traits in soybeans, with a focus on analyzing the mapping of drought resistance QTLs, the mining of candidate genes, and their application in drought resistance breeding. At the same time, combining GWAS with other molecular breeding techniques such as marker assisted selection (MAS) and genome selection (GS), we have promoted the improvement of drought resistance traits and explored the potential of gene editing technology in enhancing soybean drought resistance. Research has found that GWAS has made significant progress in the study of soybean drought resistance, identifying multiple key QTLs that affect root development, water use efficiency (WUE), and metabolic pathways, and revealing the impact of gene environment interactions on drought resistance traits. Through gene functional analysis, candidate genes for drought resistance and their regulatory networks have been identified, providing a new direction for molecular breeding of drought resistant traits. GWAS has demonstrated strong potential in the study of drought resistant traits in soybeans, not only revealing complex genetic regulatory networks, but also providing valuable molecular tools for drought resistant breeding. In the future, by integrating new technologies such as big data, machine learning, and gene editing, precision breeding of drought resistant traits will be further optimized and promoted, providing more adaptable varieties for global soybean production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".