Identification of Quantitative Trait Loci (QTL) Associated with Seed Soyasaponin I Concentration in Soybean (Glycine max [L] Merril.)
Bibliographic record
Abstract
Soybean (Glycine max [L.] Merr.) is the world’s largest oilseed crop and also produces soyasaponins, which have nutraceutical properties. Soyasaponin I is the major soyasaponin derived from soybean seeds. Seed soyasaponin I concentration is a quantitative trait, which can be improved through marker-assisted selection. A population of 186 F4-derived recombinant inbred lines from the cross of ‘OAC Wallace’ and ‘OAC Glencoe’ and a trial of 40 soybean cultivars were used in this study. Six QTL associated with soyasaponin I were identified through simple and composite interval mapping. Genotype, environment, and genotype-by-environment interactions for soyasaponin I were significant in the cultivar trial and genotype was significant in the mapping population. These results contribute to a better understanding of the genetics of soyasaponin I, the influence of environment and genotype-by-environment interactions on the trait, and provide molecular markers to facilitate marker-assisted selection for soybean cultivars with improved soyasaponin I profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".