Isoflavone content of soybean cultivars grown in eastern Canada
Bibliographic record
Abstract
Abstract Soybean (Glycine max (L) Merr) seeds contain isoflavones that have positive impacts on human health. The objective of this study was to determine isoflavone concentrations of early maturing soybean cultivars grown in different environments and to determine their relation to other important seed characteristics. Twenty soybean cultivars were grown in replicated trials at two sites in Montreal, Canada in 2002/2003 and their total and individual isoflavone concentrations were determined by high‐performance liquid chromatography. Seed yield, 100‐seed weight and oil, crude protein and crude fiber concentrations were concurrently determined. Total and individual isoflavone concentrations were significantly affected by cultivar, site and year. Total isoflavone concentration ranged from 360 to 2241 µg g−1 and averaged 851 µg g−1 across environments and cultivars. Variation across environments ranged from 20 to 100% for specific cultivars. Total isoflavone concentration was, on average, 40% greater in 2003 than 2002, which was characterized by above average temperatures and severe drought. Despite significant cultivar × year × site interactions, cultivars with consistently high and low isoflavone concentrations across environments were identified. Weak but significant positive correlations were observed between total isoflavone concentration and seed yield, 100‐seed weight and crude fiber, indicating that isoflavone concentration is positively associated with other desirable seed characteristics. Copyright © 2004 Society of Chemical Industry
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".