Application of molecular markers in parental selection in soybean
Bibliographic record
Abstract
The rate of genetic gain in the quantity and quality of soybean grain depends considerably on the genetic diversity of the selected parental components. Genetic diversity assessment is a crucial aspect of breeding that maximizes genetic improvement. The objectives of this study were to evaluate the genetic diversity of the selected soybean germplasm using genetic markers, as well as to compare the effectiveness of breeding procedures with and without the use of genetic markers in parental selection. The genetic relationships within the selected soybean germplasm were estimated using 14 simple sequence repeats (SSRs). The agronomic performance (grain yield, protein and oil content in the grain) of the parental components and derived lines was determined in field trials. Based on SSR marker data and phenotypic data, an association was found between the agronomic performance of the derived lines and the genetic distance between the parental lines. Crosses between more diverse parents resulted in derived lines with greater values for grain yield and grain quality compared with the parents than crosses between similar parents. The results indicated the usefulness of genetic marker information in parental selection, contributing to breeding efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".