Estimating pesticide environmental concentrations in Latin America: The importance of developing local scenarios
Bibliographic record
Abstract
Abstract Data to assess pesticide exposure in soil and water are scarce and unevenly distributed in Latin America, especially due to the size of the region and the vast agricultural landscape. This makes it difficult to assess associated environmental risks. We suggest that the lack of pesticide exposure or monitoring data can be addressed by using validated models to provide estimated pesticide exposure concentrations in soil and water bodies. This exposure modeling approach has been used by regulatory agencies in other countries and regions such as the United States, Canada, and the European Union. In order to properly estimate pesticide exposure concentrations, we advocate for the development of local scenarios containing local weather, soil, and crop data to be used in the existing models. A sensitivity analysis of the models can be performed to determine parameters that are sensitive and therefore inputs to these parameters are derived locally. We believe the development of local scenarios in the region is attainable and can be a pragmatic approach for developing a more comprehensive picture of potential pesticide exposure in the region. Integr Environ Assess Manag 2021;17:901–904. © 2021 Syngenta Proteção de Cultivos Ltda KEY POINTS Pesticide exposure modeling can be an alternative to fulfill the gap on pesticide monitoring data. Developing local scenarios for use in modeling tools is important to support local risk assessment. Modeling tools should be well tested and validated prior to adoption. A sensitivity analysis is important to determine the parameters of importance.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".