Cultivar, Planting Date, and Row Spacing Effects on Mungbean Seed Composition
Bibliographic record
Abstract
Mungbean [(Vigna radiata (L.) R. Wilczek, Fabaceae] is becoming an important food crop in the United States of America. This crop has previously been produced in the US states of Texas and Oklahoma but this production is currently not significant. Recent efforts have established that mungbean can be easily produced in Virginia, located in the mid-Atlantic region of the United States of America. However, there is a complete lack of information related to nutritional quality of mungbean produced in this region. We grew mungbean during 2012 and 2013 using two cultivars (Berken and TexSprout), two planting dates (early and late July), and two row spacings (0.375 and 0.75 m) to characterize composition of mungbean seed produced in Virginia. Mungbean seeds produced in this study averaged 1.59, 24.3, and 4.91% oil, protein, and sugars, respectively. These mungbean seeds also contained 38.8, 61.2, 5.79, and 55.1% in saturated, unsaturated, mono-unsaturated, and poly-unsaturated fatty acids, respectively. Predominant fatty acids in the mungbean seed were C16:0 (26.1%), C18:0 (6.11%), C18:2 (36.8%), and C18:3 (18.3%). Iron and zinc contents of the mungbean seed were 8.42 and 3.88 mg·100 g-1. Concentrations of fructose, glucose, sucrose, raffinose, stachyose, and verbascose sugars in mungbean seed were 0.45, 0.30, 0.70, 0.24, 0.84, and 2.37%, respectively. Effects of cultivars, planting dates, and row spacings on mungbean seed composition were, generally, not significant. Overall, mungbean seed compared well with nutritional quality of kidney bean, pinto bean, navy bean, and tepary bean.
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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) | 0.001 | 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".