Avaliação de risco à biodiversidade em comparação às regulamentações internacionais de pesticidas nas águas superficiais da bacia hidrográfica do rio Dourados
Bibliographic record
Abstract
The present study addressed an analysis, from a risk assessment perspective, of pesticide contamination in the surface waters of the Rio Dourados Watershed, located in Mato Grosso do Sul, Brazil, resulting from intensive agricultural practices in the region. Water contamination data were provided by Embrapa, and the aquatic biodiversity risk assessment was conducted following a comprehensive review of the main global pollutant limit regulations. This review included a comparison of the legislation of various countries, such as Brazil, Canada, China, the United States, Australia, the European Union, as well as the guidelines of the World Health Organization (WHO). The comparison of environmental regulations revealed significant discrepancies in the maximum allowable pollutant limits in water resources, highlighting vulnerabilities in current standards and the need for harmonization to achieve more effective protection of water resources. The biodiversity risk assessment was based on the calculation of the risk quotient, identifying potential harm to aquatic organisms. The results also emphasize the importance of sustainable agricultural practices and continuous water quality monitoring to mitigate the negative impacts of pesticides. This study contributed to a deeper understanding of the impacts of pesticides on water quality and the health of aquatic ecosystems. Finally, the analysis promotes a crucial discussion on environmental management and legislation involving pollutants and highlights the need for stricter regulations to protect water resources and biodiversity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".