Environmental and Genetic Variation in Essential Mineral Nutrients and Nutritional Value Among Brassica Vegetables
Bibliographic record
Abstract
Dietary minerals play an important role in human nutrition and proper metabolism. We grew various Brassica crops under field conditions in 2012 and 2013 and analyzed 8 essential minerals from edible tissues of those crops. Among the investigated crops, pak choi (Brassica rapa), mustard greens (B. juncea; B. nigra), and komatsuna (B. rapa) were generally high in most minerals, according to dry weight-based concentrations. The percentage recommended daily intake (RDA) or adequate intake (AI) values, calculated using fresh weight-based concentrations, suggest that Brassica vegetables are a good source of iron, calcium, and manganese, providing > 20% of %RDA/AI depending on crop. Kale (B. oleracea; B. napus) was generally higher in %RDA/AI, in particular for calcium (Ca), phosphorous (P), magnesium (Mg), and manganese (Mn). From the 2-year study, days to harvest, growing degree days, total solar radiation, and total precipitation and evaporation were found to affect the concentration of Ca, P, Ma, and Me. The results of this study provide a direct comparison of the mineral composition of various Brassica crops grown under the same conditions and will help consumers’ food choice for better nutritional value.
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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 | 0.000 | 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".