Genetic Dissection of Soybean [Glycine max (L.) Merr.] Seed Composition in Populations Derived from Edamame × Food-Grade Crosses
Bibliographic record
Abstract
Soybean seed size and concentrations of protein, oil, sucrose, stachyose, and raffinose are major breeding targets to improve quality. The objectives of this study were to quantify genetic, environment, and genotype-by-environment effects on the stated traits, and to identify and validate quantitative trait loci (QTL) associated with them. Recombinant inbred lines (RILs) derived from two crosses between high-performing food-grade soybeans and imported high-protein edamame (MF1 x OAC Ramsay, and MF1 x DH410) were grown in four Southern Ontario environments. Single nucleotide polymorphism (SNP) markers were generated for RILs using genotyping-by-sequencing. Construction of linkage groups and QTL analyses were done using IciMapping software. Significant genotype-by-environment interactions were found for seed size but no other traits. In total, 94 QTL were discovered underlying the traits of interest, 14 of which were identified in another population or environment. The findings of this study can assist breeders in developing cultivars with improved seed quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".