Improving nutrition through biofortification–A systematic review
Bibliographic record
Abstract
Nutritious foods are essential for human health and development. However, malnutrition and hidden hunger continue to be a challenge globally. In most developing countries, access to adequate and nutritious food continues to be a challenge. Although hidden hunger is less prevalent in developed countries compared to developing countries where iron (Fe) and zinc (Zn) deficiencies are common. The United Nations (UN) 2nd Sustainable Development Goal was set to eradicate malnutrition and hidden hunger. Hidden hunger has led to numerous cases of infant and maternal mortalities, and has greatly impacted growth, development, cognitive ability, and physical working capacity. This has influenced several countries to develop interventions that could help combat malnutrition and hidden hunger. Interventions such as dietary diversification and food supplementation are being adopted. However, fortification but mainly biofortification has been projected to be the most sustainable solution to malnutrition and hidden hunger. Plant-based foods (PBFs) form a greater proportion of diets in certain populations; hence, fortification of PBFs is relevant in combating malnutrition and hidden hunger. Agronomic biofortification, plant breeding, and transgenic approaches are some currently used strategies in food crops. Crops such as cereals, legumes, oilseeds, vegetables, and fruits have been biofortified through all these three strategies. The transgenic approach is sustainable, efficient, and rapid, making it suitable for biofortification programs. Omics technology has also been introduced to improve the efficiency of the transgenic approach.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".