Probabilistic risk assessment of dietary exposure to aflatoxin B1 in Guangzhou, China
Bibliographic record
Abstract
Abstract Aflatoxin B1 (AFB1) contamination in foods is an important health challenge for low-and middle-income countries in subtropical regions. AFB1 has been detected in a variety of foodsin Guangzhou, while the risk of dietary exposure is unknown. This study aimed to assess the probabilistic risk of dietary exposure to AFB1 contamination in food stuffs in Guangzhou by using margin of exposure (MOE) and quantitative liver cancer risk approaches. A total of1854 AFB1-contaminated foodstuffs were sampled in supermarkets, agricultural markets, retail shops, and family workshops from 11 districts of Guangzhou, and AFB1 content was determined by HPLC-fluorescence detector. In total, 9.9% (184/1854) of the test samples had AFB1 concentrations above the limit of detection. Home-made peanut oil had the highest AFB1 concentration, with a mean value of 38.74 ± 47.45 μg kg−1. The average MOE levels of Guangzhou residents ranged from 100 to 1000. The risk of liver cancer was 0.0264 cancers (100,000 population year)−1. The health risks of suburban people were higher than those of urban people, and home-made peanut oil was the main contributorto dietary exposure to AFB1 among suburban residents in Guangzhou. The production of home-made peanut oil should be supervised to reduce the risk of AFB1 exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".