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Record W4292860085 · doi:10.5539/jsd.v15n5p57

Reflection on Africa's Underutilized Foods Towards a Sustainable Food System

2022· article· en· W4292860085 on OpenAlexvenueno aff
K. M. Munoko, R. Kombat, K. A. Kofi, S. P. Ramid, A. O. Fatunbi

Bibliographic record

VenueJournal of Sustainable Development · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityLivelihoodFood systemsBusinessAgricultureStakeholderInvestment (military)Stakeholder engagementFood processingSustainable agricultureIndigenousNatural resource economicsSustainabilityEconomic growthEconomicsPolitical scienceGeographyPublic relationsEcologyManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.232
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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