Probabilistic risk assessment of cotton pyrethroids: V. Combining landscape-level exposures and ecotoxicological effects data to characterize risks
Bibliographic record
Abstract
Since their introduction, synthetic pyrethroid insecticides have generated regulatory concerns regarding their toxicity to fish and aquatic invertebrates. In this paper we assess the potential for risks to aquatic ecosystems in cotton-growing areas, focusing on cypermethrin as a suitable representative of the pyrethroid class and static water bodies (ponds and lakes) as worst-case water bodies because of low levels of dilution. Reviews of cypermethrin effects under laboratory and field conditions have characterized the potential aquatic effects of the chemical. Also, a landscape-level exposure characterization has been conducted in a worst-case cotton-growing county, Yazoo County, Mississippi, USA, to provide a more realistic exposure characterization than is possible using standard model scenarios. Risks were characterized using the standard tier I and II approaches of the U.S. Environmental Protection Agency. In addition, a probabilistic risk assessment was conducted by comparing landscape-level exposure calculations for ponds and lakes in Yazoo County (modified tier II analysis) with distributions of laboratory effect concentrations and with data from field studies. Risk characterization using tier I and tier II models demonstrated a level of concern for certain aquatic organisms. However, modified tier II analysis showed that exposure concentrations are unlikely to exceed concentrations that might cause ecologically significant effects. Indeed, in the vast majority of cases, concentrations in the modified tier II analysis were several orders of magnitude lower than those at which effects would be predicted on the basis of laboratory and field data. The conclusion of minimal potential for adverse ecological effects was also supported by field studies, which showed that impacts on aquatic systems were negligible, even at concentrations many times higher than the modified tier II exposure concentrations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".