Development and scale-up of bioprotectants to keep staple foods safe from aflatoxin contamination in Africa
Bibliographic record
Abstract
Aflatoxins pose a significant public health risk, decrease productivity and profitability and hamper trade. To minimize aflatoxin contamination a biocontrol technology based on atoxigenic strains of Aspergillus flavus that do not produce aflatoxin is used widely in the United States. The technology, with the generic name Aflasafe, has been improved and adapted for use in Africa. Aflasafe products have been developed or are currently being developed in 20 African countries. Aflatoxin biocontrol is being scaled up for use in several African countries through a mix of public, private, and public-private interventions. Farmers in several countries have commercially treated nearly 400,000 ha of maize and groundnut achieving >90% reduction in aflatoxin contamination. This chapter summarizes the biology of aflatoxin-producing fungi and various factors affecting their occurence, including climate change. Various management practices for aflatoxin mitigation are then discussed. These include biological control, which is increasingly being adopted by farmers in several countries. We discuss biocontrol product development and commercialization in various African countries. Subsequently, we highlight some barriers to adoption and other challenges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".