Challenges with the current methodology for conducting Endangered Species Act risk assessments for pesticides in the United States
Bibliographic record
Abstract
The US Environmental Protection Agency (USEPA or the Agency) is responsible for administering the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA). The Agency is also required to assess the potential risks of pesticides undergoing registration or re-registration to threatened and endangered (i.e., listed) species to ensure compliance with the Endangered Species Act. To assess potential risks to listed species, a screening-level risk assessment in the form of a biological evaluation (BE) is undertaken by the Agency for each pesticide. Given the large number of registration actions handled by the USEPA annually, efficient tools for conducting BEs are desirable. However, the "Revised Method" that is the basis for the USEPA's BE process has been ineffective at filtering out listed species and critical habitats that are at de minimis risk to pesticides. In the USEPA's BEs, the Magnitude of Effect Tool (MAGtool) has been used to determine potential risks to listed species that potentially co-occur with pesticide footprints. The MAGtool is a highly prescriptive, high-throughput compilation of existing FIFRA screening-level models with a geospatial interface. The tool has been a significant contributor to risk inflation and ultimately process inefficiency. The ineffectiveness of the tool stems from compounding conservatism, unrealistic and unreasonable assumptions regarding usage, limited application of species-specific data, lack of consideration of multiple lines of evidence, and inability to integrate higher-tier data. Here, we briefly describe the MAGtool and the critical deficiencies that impair its effectiveness, thus undermining its intention. Case studies are presented to highlight the deficiencies and solutions are recommended for improving listed species assessments in the future. Integr Environ Assess Manag 2023;19:817-829. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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 | 0.002 | 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 | 0.000 |
| 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".