Estimating the Toxicity of Pesticide Mixtures to Aquatic Organisms: A Review
Bibliographic record
Abstract
ABSTRACT A major difficulty in addressing chemical mixtures through legislation or regulations revolves around our limited understanding of their potential impacts. This review provides an overview of recent research on pesticide mixture toxicity to aquatic biota and the methods employed to predict toxic effects. The most common approaches are to assume concentration-addition or independent action of chemicals in a mixture. There are a number of cases in the literature of interactions between pesticides. However, models accounting for possible interactions between mixture components are used infrequently. Although results are limited, studies investigating the effects of pesticide mixtures have not demonstrated significant synergism at environmentally relevant concentrations. Based on the results of our review, we conclude that the concentration-addition model is a generally conservative and practical first-tier model for the ecological assessment of pesticide mixtures in aquatic systems. Key Words: pesticidesmixture toxicityconcentration-addition. ACKNOWLEDGMENTS This research was funded by Crop Life America.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".