Energy and nutrient intake of a group of adolescent girls from Benin.
Bibliographic record
Abstract
Energy and nutrient intakes are important health variables particularly during adolescence. The dietary intake of 100 adolescent girls aged 14–16 years from Benin was investigated. Fifty adolescents were boarding at the school, while 50 lived at home. Dietary intakes were obtained by a 48‐hour recall and absorbable iron intakes were estimated using Monsen's model. The following methods were used to estimate inadequacy in nutrient intakes: the AI (calcium and vitamin D) and EAR cut‐point method (for all other nutrients except iron). The probability approach was used to estimate inadequacy in iron intake. Energy intakes were compared to the Total Energy Expenditure calculated for each subject using age, height, weight and physical activity level. Girls boarding at the school had significantly higher intakes of energy and nutrients compared to girls living at home, except for vitamin A, E and folate. While 73% of adolescents met the recommendation for dietary iron intake, only 27% had estimated absorbable iron intake above the average requirement for absorbed iron. Also, 95–100% of subjects had intakes of vitamin E, folate, calcium, phosphorus and magnesium below the recommendations, while these percentages varied between 59–85% for thiamine, riboflavin, vitamin C, A and zinc and between 14–33% for energy, proteins, selenium, niacin and vitamin B 12 . Thus, dietary improvement, including increased consumption of micronutrient‐rich foods are greatly needed in these young adolescents.
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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) | 0.002 | 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".