Variability in Nontarget Terrestrial Plant Studies Should Inform Endpoint Selection
Bibliographic record
Abstract
Abstract Inherent variability in nontarget terrestrial plant (NTTP) testing of pesticides creates challenges for using and interpreting these data for risk assessment. Standardized NTTP testing protocols were initially designed to calculate the application rate causing a 25% effect (ER25, used in the United States) or a 50% effect (ER50, used in Europe) for various measures based on the observed dose–response. More recently, the requirement to generate a no-observed-effect rate (NOER), or, in the absence of an NOER, the rate causing a 5% effect (ER05), has raised questions about the inherent variability in, and statistical detectability of, these tests. Statistically significant differences observed between test and control groups may be a product of this inherent variability and may not represent biological relevance. Attempting to derive an ER05 and the associated risk-assessment conclusions drawn from these values can overestimate risk. To address these concerns, we evaluated historical data from approximately 100 seedling emergence and vegetative vigor guideline studies on pesticides to assess the variability of control results across studies for each plant species, examined potential causes for the variation in control results, and defined the minimum percent effect that can be reliably detected. The results indicate that with current test design and implementation, the ER05 cannot be reliably estimated. Integr Environ Assess Manag 2018;14:639–648. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points Inherent variability in nontarget terrestrial plant (NTTP) testing of pesticides creates challenges for risk assessment. An evaluation of approximately 100 seedling emergence and vegetative vigor guideline studies was performed to assess the variability in control results and define the minimum percent effect that can be reliably detected. With current NTTP test design and implementation, it will often be impossible to reliably estimate an ER05, and it will rarely be possible to reliably estimate an ER10.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".