Contribution to the improvement of a porridge made with fermented maize: effect of selected foods and lemon on energy density, pH, viscosity and nutritional quality
Bibliographic record
Abstract
The objective of the present study was to use lemon and selected foods to improve the nutritional characteristics, quality and the nutrient content of a traditional complementary porridge made of lactic acid fermented yellow maize. Boiled egg yolk, roasted peanut paste, dry crayfish flour, roasted soybean flour and lemon juice were used as food additions. Amounts of food added were calculated on the basis of World Health Organization estimated energy needs from complementary foods of well-nourished children in developing countries, aged 9-11 months, at four servings per day and a low amount of breast milk energy. The pH and viscosity increased in porridges with food addition, but lemon juice contributed to lowering them. Energy and nutrient densities/100 g porridge improved with food addition regardless of the use of lemon juice. An increase in iron, zinc and calcium in vitro availability was observed (P < 0.05) with the addition of lemon juice.
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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".