Genetic Diversity of Legume Germplasm Resources and Their Application in High-Yield Breeding
Bibliographic record
Abstract
As an important food and feed resource, legume crops play an important role in ensuring food security, improving soil fertility and promoting sustainable agricultural development. Rich germplasm resources provide a key genetic basis for high-yield breeding of legumes. This study systematically sorted out the diversity characteristics of the current main germplasm resources of legumes, covering their geographical distribution, phenotypic variation and genetic background, and focused on analyzing important agronomic traits related to high yield, such as pod number, grain weight, stress resistance and nitrogen fixation ability. It further explored the specific application paths of diversity resources in modern breeding, including the introduction of excellent alleles, the development of pre-breeding materials, and the integration of marker-assisted selection and genomic selection technology. At the same time, the actual value of diversity germplasm in improving breeding efficiency and yield performance was explained through typical high-yield breeding cases. Fully exploring and accurately utilizing the genetic diversity of legumes is one of the core strategies to promote high-yield, stable yield and green development of legumes. This study hopes to provide theoretical support and practical reference for the construction of efficient breeding systems and the creation of new germplasm in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".