Integrating Exposure and Effect Distributions with the Ecotoxicity Risk Calculator: Case Studies with Crop Protection Products
Bibliographic record
Abstract
Abstract Risk curves describe the relationship between cumulative probability and magnitude of effect and thus express far more information than risk quotients. However, their adoption has remained limited in ecological risk assessment. Therefore, we developed the Ecotoxicity Risk Calculator (ERC) to simplify the derivation of risk curves, which can be used to inform risk management decisions. Case studies are presented with crop protection products, highlighting the utility of the ERC at incorporating various data sources, including surface water modeling estimates, monitoring observations, and species sensitivity distributions. Integr Environ Assess Manag 2021;17:321–330. © 2020 Syngenta Crop Protection, LLC. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC) KEY POINTS Risk curves describe the relationship between distributions of exposure and effect. The Ecotoxicity Risk Calculator can be used to generate risk curves from various data sources. Case studies are presented with risk curves for crop protection products.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".