Tocopherols Concentration and Stability in Early Maturing Soybean Genotypes
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.] is an important source of tocopherols which have health‐beneficial properties. A study was conducted to determine tocopherols concentration and stability in early maturing soybean genotypes and to determine their relationships to other important seed characteristics. Twenty soybean genotypes were grown in replicated trials in six environments of eastern Canada, and their total‐, α‐, γ‐, and δ‐tocopherol concentrations were determined by high‐performance liquid chromatography. Seed yield, 100‐seed weight, and crude protein and oil concentrations were concurrently determined. Genotype and environment main effects and G × E interactions were observed for all tocopherols. Variations in the ranking of genotypes were, however, limited. Across environments and genotypes, relative proportions of α‐, δ‐, and γ‐tocopherol were 6, 34, and 60%, respectively. Variation in α‐tocopherol among genotypes was significant, with concentrations ranging between 8.7 and 33.2 μg g−1 across environments. Differences between environments were significant, albeit minimal, for all tocopherols; there was a 42% average variation in α‐tocopherol concentration across environments. Stability of genotypes for α‐tocopherol was not linked to mean concentration; however, some genotypes with high stability and mean concentration were identified, including the cultivar Heron. No correlations were observed between α‐tocopherol and any of the other seed characteristics investigated. However, negative correlations were observed between the other tocopherols and seed yield, 100‐seed weight, and crude protein. Tocopherols were positively correlated with each other, except for a negative correlation between α‐ and δ‐tocopherol. The large variation observed among genotypes for α‐tocopherol, the relatively high stability of genotypes performance across environments, and the lack of negative correlation with other important seed characteristics suggest that selection for high α‐tocopherol could be possible.
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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".