Bibliographic record
Abstract
One in three people in the developing world suffer from 'hidden hunger', or micronutrient deficiency, due to a lack of information on proper nutrition.This is a major cause of illness, poor growth, reduced productivity and impaired cognitive development.To help combat the problem, CABI and its partners developed content for multiple mobile-phone based messaging services during a three year mNutrition initiative, which aimed to increase knowledge of nutrition, health and agriculture in 12 countries across Africa and Asia.As lessons were learnt about content management processes, content was also delivered to millions of farmers through Mobile Network Operator (MNO)-led, content driven services, delivering agriculture or health information.This case study presents lessons from all 12 countries and results of an impact assessment carried out by GSMA for six agriculture Value Added Services (VAS). Key highlights• Localised content created in 12 countries.• Content available in 24 local languages.• Freely accessible Nutrition Knowledge Bank.• Over 12,000 messages produced.• Over 1,500 factsheets produced.• 12 local partners trained in quality content development.• Services developed under mNutrition's mAgri component cumulatively reached more than 5 million registered users worldwide.• Over 1.5 million mAgri users made changes to their behaviour, of which 36% made on-farm changes, including planting, land management, and harvest and storage practices.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".