Quantitative trait loci underlying the development of seed composition in soybean (<i>Glycine max</i>L. Merr.)
Bibliographic record
Abstract
One hundred and forty-three F2:7 recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were analyzed for the quantitative trait loci (QTLs) underlying protein or oil content at 6 different developmental stages by composite interval mapping with a mixed genetic model. The genotype x environment (GxE) interactions of the QTLs were also evaluated. Nineteen (2004) and 33 (2005) unconditional QTLs underlying seed protein or oil content at the different developmental stages were mapped onto 8 and 9 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 6.26% to 30.52% and from 5.38% to 28.47%, respectively. Fourteen (2004) and 21 (2005) conditional QTLs underlying seed protein or oil content were mapped onto 5 and 8 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 2.97% to 29.68% and from 5.42% to 31.96%, respectively. The numbers and types of QTLs and the genetic effect for the two traits were different at each developmental stage. However, several genomic regions that simultaneously control the development of both traits were detected. The genetic effect on protein content and oil content was opposite for loci in the marker interval Satt335-SSatt334, reflecting a negative correlation of protein content and oil content. A G x E interaction effect of some QTLs underlying protein or oil content at different growth periods was observed.
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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) | 0.000 | 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".