Monitoring residues of pesticides in food in Brazil: A multiscale analysis of the main contaminants, dietary cancer risk estimative and mechanisms associated
Bibliographic record
Abstract
Introduction: Pesticides pose a risk for cancer development and progression. People are continuously exposed to such substances by several routes, including daily intake of contaminated food and water, especially in countries that are highly pesticide consumers and have very permissive legislation about pesticide contamination as Brazil. This work investigated the relationship among pesticides, food contamination, and dietary cancer risk. Methods: Analyzed two social reports from the Brazilian Government: the Program for Analysis of Residues of Pesticides in Food (PARA) and The National Program for Control of Waste and Contaminants (PNCRC). Results and discussion: First, we characterized the main pesticide residues detected over the maximum limits allowed by legislation or those prohibited for use in food samples analyzed across the country. Based on this list, we estimated the dietary cancer risks for some of the selected pesticides. Finally, we searched for data about dietary cancer risks and carcinogenic mechanisms of each pesticide. We also provided a critical analysis concerning the pesticide scenario in Brazil, aiming to discuss the food contamination levels observed from a geographical, political, and public health perspective. Exposures to pesticides in Brazil violate a range of human rights when food and water for human consumption are contaminated.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".