Crop Management,\nGenotypes, and Environmental Factors Affect Soyasaponin B Concentration\nin Soybean
Bibliographic record
Abstract
Soybean\n[<i>Glycine max</i> (L.) Merr.] seeds contain soyasaponin\nB, which has putative health benefits. Studies were conducted in multiple\nenvironments in Quebec, Canada to determine the effects of genotypes,\nenvironments, and seeding dates on soyasaponin B concentration in\nmature seeds. A growth chamber study was also conducted to determine\nthe impact of high air temperature imposed at specific growth development\nstages on soyasaponin B in soybeans. Concentrations of individual\nand total soyasaponin B were determined using high-performance liquid\nchromatography. Genotype and environment main effects were the main\ndeterminants of soyasaponin B concentration in soybean, genotype ×\nenvironment interactions accounting for less than 5% of the variation\nfor all soyasaponin. Ranking of 20 early maturing soybean genotypes\nwas thus relatively consistent across four environments, total concentration\nvarying between 2.31 and 6.59 μmol g<sup>–1</sup>. Seeding\ndate consistently impacted soyasaponin B concentrations, early seeding\ndate resulting in the highest concentrations. There was an 11% difference\nin total soyasaponin B concentration of soybeans seeded in mid-May\ncompared to that in late-June. The response to high air temperature\nwas complex and cultivar specific. High temperature stress restricted\nto the seed filling stages increased total soyasaponin B concentration\nin one cultivar by 28% when compared to that in control nonstressed\nplants; however, in another cultivar high temperature applied during\nall growth stages reduced total concentration by 27%. Results from\nthe present study thus demonstrate that environmental factors and\ncrop management both impact soyasaponin B concentration in soybeans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.089 | 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 teacher head, 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".