Reflection on Africa's Underutilized Foods Towards a Sustainable Food System
Bibliographic record
Abstract
Malnutrition is a big problem on the African continent, especially among children ages zero to twenty-four months up till a time that covers the 1st 1000 days of life. Expert opinion suggests that Africa could integrate more commodities into its food systems to solve this problem, especially using its underutilized food commodities. These commodities are known to thrive very well in their various ecological zones. Indigenous food commodities are nutrient-dense, but the limitation in their use have left their potential untapped. The Forum for Agricultural Research in Africa organized a webinar to seek African stakeholders' opinions on the pathway to integrating forgotten Food into the African food system. This study extracted information from the stakeholder’s interaction in the webinar and used an integrative literature review method to draw inferences. The study suggests that considerable efforts are required to integrate the forgotten food commodities into the African food systems to enhance food security and sustainable agricultural livelihood. The needed actions include awareness creation on the importance of the forgotten food commodities, the establishment of a robust research program, active private sector engagement for investment into production, value chain development, and investment into commodity development infrastructures and businesses.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".