Nutritional Potential of 15 Local Plant Species in Preventing Mineral Deficiencies in Young Children in Niger
Bibliographic record
Abstract
Macroelements and trace elements are essential for the body to function properly. The aim of this study was to determine the mineral composition of the products of 15 local species and their contribution to combating mineral deficiencies. Iron, phosphorus and zinc were determined by UV/visible spectrophotometer. Sodium, calcium, magnesium and potassium were determined by atomic absorption spectrophotometer. The rate of coverage of children's daily mineral requirements was determined according to Canadian government recommendations. The results show that Hyphaene thebaïca pulp contains the highest proportion of potassium (8000mg/100g M); and phosphorus in Arachis hypogaea seeds (662.72mg/100g) and Ziziphus mauritiana kernels (336.71mg/100g). Magnesium levels were highest in Adansonia digitata pulp (283.10mg/100g) and Hyphaene thebaïca (216.27mg/100g), while calcium levels were highest in Adansonia digitata pulp (194.39mg/100g) and Neocarya macrophylla kernel (128.26mg/100g). Sodium levels were highest in Arachis hypogaea seeds (344.91mg/100g). In addition, the highest iron contents were found in the kernel of Anacarduim occidentale (6.40mg/100g) and the seeds of Glycine max (5.80mg/100g) and Pennisetum glaucum (5.80mg/100g). The highest zinc levels were found in almonds, notably Anacarduim occidentale (2.30mg/100g) and Zizyphus mauritiana (2.79mg/100g). More than 80% of the products from the species in this study meet more than 45% of the daily phosphorus requirements of children aged 7 to 12 months. Almonds from Anacarduim occidentale and Neocarya macrophylla provide over 40% of daily iron and zinc requirements for all age groups. These plant products could therefore be used in strategies to combat micronutrient deficiencies and even malnutrition.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".