Detection of QTL underlying seed quality components in soybean [<i>Glycine max</i> (L.) Merr.]
Bibliographic record
Abstract
Improving seed composition and quality, including protein, oil, fatty acid, and amino acid contents, is an important goal of soybean farmers and breeders. The aim of this study was to map the quantitative trait loci (QTL) underlying the contents of protein, oil, fatty acids, and amino acids with 1510 single nucleotide polymorphism (SNP) markers using the ‘Hamilton’ × ‘Spencer’ recombinant inbred line population (H × S; n = 93). A total of 13 QTL for the traits studied have been mapped on 3 chromosomes (Chr.) of the soybean genome. Three major QTL have been mapped to a 7–13 cM region on Chr. 6. One major QTL for oil content (qOIL001) explained approximately 76% of the total phenotypic variation in this population; the second major QTL for amino acid alanine (Ala; qALA001) explained approximately 74% of the total variation in Ala content; moreover, two major QTL for palmitic acid (qPAL001 and qPAL002) were identified on Chr. 6 and explained approximately 21% of the phenotypic variation in this population. The SNP markers flanking the QTL identified here will be very useful for soybean breeders to develop and select soybean lines with higher seed composition qualities using marker-assisted selection.
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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 | 0.000 | 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".