Statistically based soil–climate exposure scenarios for aquatic pesticide fate modeling and exposure assessment in the Pampa Region of Argentina
Bibliographic record
Abstract
Abstract Although pesticides are used intensively in Argentina's Pampa region, the possibility of performing an environmental risk assessment (ERA) remains limited due to the absence of readily available databases to run pesticide fate models and the lack of standardized realistic worst-case scenarios. The aim of the present study was to further advance capacities for performing probabilistic ERAs in the Pampa region by dividing and parameterizing the region into functional soil–climate mapping units (SCU) and defining statistically based, worst-case soil–climate exposure scenarios. Results obtained demonstrate that the SCU selected for a specific modeling exercise should depend on the dissociation constant (Kd) of the pesticide evaluated and whether short- or long-term pesticide fate modeling and risk assessment are needed. Four regionally representative SCUs were specifically identified for modeling the fate of pesticides with low, high, and intermediate values of Kd. Fate modeling of pesticides with an intermediate Kd requires the use of a different SCU for short- versus long-term pesticide modeling, whereas this distinction is not necessary for pesticides with both low and high Kd. The current definition of realistic, worst-case, soil–climate scenarios represents a crucial step toward better pesticide fate modeling and exposure assessment in Argentina's Pampa region. Integr Environ Assess Manag 2023;19:626–637. © 2022 SETAC
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".