Nutritional Evaluation of Five African Indigenous Vegetables
Bibliographic record
Abstract
Abstract The promotion and consumption of indigenous vegetables could help mitigate food insecurity and alleviate malnutrition in developing countries. In this respect, 17 accessions (candidate breeding lines that have not yet officially been released) of five African indigenous vegetables: amaranth (Amaranthus cruentus), nightshade (Solanum scabrum), African eggplant (Solanum aethiopicum), jute mallow (Corchorus olitorius) and okra (Abelmoschus callei), previously selected for their superior agronomic and horticultural traits, were evaluated in Cameroon for minerals (Ca, Mg, K, P, Zn and Fe), proteins, and carotenoids content. Nutrient content differed significantly (P<0.001) between cultivars. Amaranth (especially line AM-NKgn) had the highest Ca, Mg, and Zn content in comparison to other genotypes studied. Nightshade had the highest K and Fe content. The highest K and Fe levels were found in nightshades BG24 and SS52, respectively. Nightshade had the highest level of protein, especially line BFS1. The highest amount of carotenoids was identified in the eggplant variety Oforiwa. The study revealed that these vegetables are important sources of some vital nutrients. Increased production and consumption of these nutrient-rich vegetables will help reduce the nutrition-related disorders in Africa.
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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".