Factors affecting isoflavone concentration in soybean (Glycine max L.)
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.] seeds contain isoflavones that have positive impacts on human health. Field and greenhouse experiments were conducted in Quebec Canada to determine the effects of management and environmental factors [seeding date (late May and mid June), row spacing (20-, 40- and 60-cm), weeds (presence or absence), irrigation levels (low, moderate, and high) and genotypes (Proteina, Orford, and Golden)] and of foliar applications of elicitor compounds (i.e., LCOs, chitosan, and actinomycetes spores), on the isoflavone concentrations of mature soybean seeds, and other important seed characteristics. Our results indicated that environmental and agronomical factors have a great impact on soybean seed isoflavone concentrations of early maturity soybean cultivars. Year, seeding date, and weeds affected total and individual isoflavone concentrations, row spacing had no effect. Total isoflavone concentration was greater in 2003 than 2004. Seeding in mid June increased isoflavone concentration by 38%, compared to seeding in May. The presence of weeds increased total isoflavone concentrations by 9%. Isoflavone concentrations were significantly affected by cultivars and irrigation levels. In both of two growing seasons, Proteina had significantly greater isoflavone concentrations compared to Orford. Irrigation effects on isoflavone concentrations differed between years and cultivars. However, most responses were observed with the lower of the two irrigation levels, which increased isoflavone concentrations by as much as 60% compared to a non-irrigated control. Our results suggest that under greenhouse conditions most biotic elicitors tested increased the concentration of individual and total isoflavones in soybean seeds when compared to untreated control plants. LCOs proved to be the most effective in studies contrasting various elicitors. Response of field-grown plants was more variable than that of greenhouse-grown plants.
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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".