Bibliographic record
Abstract
Mycotoxins are a group of toxic secondary metabolites produced in the food chain by fungi through the infection of crops both before and after harvest. Mycotoxins are one of the most important food safety concerns due to their severe poisonous and carcinogenic effects on humans and animals upon ingestion. In the last decade, insects have received wide attention as a highly nutritious, efficient and sustainable source of animal-derived protein and caloric energy for feed and food purposes. Many insects have been used to convert food waste into animal feed. As food waste might contain mycotoxins, research has been conducted on the metabolism and detoxification of mycotoxins by edible insects. The mycotoxins that have been studied include aflatoxins, fumonisins, zearalenone (ZEN), vomitoxin or deoxynivalenol (DON), and ochratoxins (OTAs). Aflatoxin metabolism is proved through the production of hydroxylated metabolites by NADPH-dependent reductases and hydroxylases by different insects. ZEN can be metabolized into α- and β-zearalenol. Three DON metabolites, 3-, 15-acetyl-DON, and DON-3-glucoside, have been identified in the insect DON metabolites. Unfortunately, the resulting metabolites, involved enzymes, and detoxification mechanisms of OTAs and fumonisins within insects have yet to be identified. Previous studies have been focused on the insect tolerance to mycotoxins and the produced metabolites; further research needs to be conducted to understand the exact enzymes and pathways that are involved.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".