Bibliographic record
Abstract
Soybean ( Glycine max [L.] Merr.) holds an important position worldwide due to its high protein and oil content, and is a key source of human consumption and animal feed. However, soybean cultivation is confronted with the challenges of climate change and the need to increase yield and stress resistance. Genome-wide association studies (GWAS) are of great value in identifying key genetic loci associated with complex agronomic traits, including yield, stress resistance, nutritional quality and disease resistance. This review summarizes the progress made in soybean genomics through GWAS and elaborates on the loci and candidate genes that affect traits such as seed composition, plant height, and root development. Integrating the findings of GWAS into molecular breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS) can promote the development of high-yield and climate-adapted soybean varieties. Furthermore, the combination of GWAS with advanced genomic tools and computational methods provides insights for future research. These research findings contribute to the sustainable improvement of soybean productivity to address the urgent need for global food sec。urity under environmental challenges
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".