Microbial strategies to control aflatoxins in food and feed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aflatoxins are a group of toxic and carcinogenic fungal metabolites. They are commonly found in cereals, nuts and animal feeds and create a significant threat to the food industry and animal production. Several strategies have been developed to avoid or reduce harmful effects of aflatoxins since the 1960s. However, prevention of aflatoxin contamination pre/post harvest or during storage has not been satisfactory and control strategies such as physical removing and chemical inactivating used in food commodities have their deficiencies, which limit their large scale application. It is expected that progress in the control of aflatoxin contamination will depend on the introduction of technologies for specific, efficient and environmentally sound detoxification. The utilisation of biological detoxification agents, such as microorganisms and/or their enzymatic products to detoxify aflatoxins in contaminated food and feed can be a choice of such technology. To date, many of the microbial strategies have only showed reduced concentration of aflatoxins and the structure and toxicity of the detoxified products are unclear. More attention should be paid to the detoxification reactions, the structure of biotransformed products and the enzymes responsible for the detoxification. In this article, microbial strategies for aflatoxin control such as microbial binding and microbial biotransformation are reviewed.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it