Mycotoxins contaminations in Ethiopian food: Impacts, challenges, and mitigation strategies
Bibliographic record
Abstract
Mycotoxins are significant contaminants in food and agricultural commodities, particularly in developing countries like Ethiopia, where regulatory measures for mycotoxin control are inadequate. Mycotoxin contamination poses substantial risks to human and animal health, and economic stability in these regions, potentially adversely affecting food availability and security. This review aimed to assess the mycotoxin contamination status in Ethiopian foods, its impacts, factors contributing to its contamination, challenges to control it, and mitigation strategies in Ethiopian foods and agricultural commodities. Several notable mycotoxins have been found in various food items, and the levels of many of these mycotoxins are higher than the maximum allowable levels of FAO/WHO and EU. Different mitigation strategies are recommended, including agricultural improvements, and physical, chemical, and agronomic approaches, tailored for affordability among low-income farmers. The review concludes with proposals for sustained public awareness campaigns and enhanced technical and human capacity development within the country.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".