How Protective to the Environment is the Pesticide Risk Assessment and Registration Process in the United States?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The media, public, and other stakeholders are generally unaware of the degree of protection provided to the environment by the current pesticide registration process in the United States. Each pesticide product must meet extensive fate and toxicological data requirements (typically 100+ studies) to be considered by the U.S. Environmental Protection Agency (EPA). The EPA uses that information to conduct ecological, human health, and benefits assessments and make decisions on whether to register pesticides and, if so, under what conditions. The assessments rely on conservative assumptions, models, and inputs to consistently err on the side of caution throughout the pesticide registration process. The rigorous compliance requirements specified in the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and Endangered Species Act (ESA) are designed to preclude unacceptable adverse effects. However, this reality seldom, if ever, makes headlines. Pesticides are not causing the dire widespread apocalyptic effects often portrayed by some media outlets. Rather, pesticides have been doing what they were intentionally designed to do, controlling pests and increasing yields, within the stringent limitations of registered labels. The continually evolving pesticide registration process was originally predicated on the unintended adverse effects neither anticipated nor considered over 50 years ago, due to insufficient regulation and oversight at the time. However, the contemporary regulatory paradigm in the U.S. is data rich and analysis intensive by design, and perhaps understandably, biased towards ensuring environmental protection when registering pesticides. https://doi.org/10.21423/jrs-v09i2moore
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it