ESASeedPARAM: A seed treatment model for threatened and endangered bird species in the United States
Bibliographic record
Abstract
The USEPA, National Marine Fisheries Service, and Fish and Wildlife Service are required to assess the risks of pesticides undergoing registration or reregistration to threatened and endangered (i.e., listed) species. Currently, the USEPA lacks a refined model to assess the risks of seed treatments to listed bird species. We developed the Endangered Species Assessment Seed Treatment Probabilistic Avian Risk Assessment Model (ESASeedPARAM) to incorporate species-specific diets, body weights, and food ingestion rates for potentially exposed listed bird species. The model also incorporates information on dissipation of seed residues after planting, and metabolism and elimination by birds during exposure. The ESASeedPARAM estimates hourly intake from ingestion of treated seeds for up to 50 days after planting. For each simulated bird, maximum retained dose (= body burden) and maximum rolling average total daily intake are estimated for acute and chronic exposure, respectively. The model is probabilistic and estimates exposure and risk for 20 birds on each of 1000 fields. The model accounts for interfield variation in the amount of waste grain on the soil surface in tilled, reduced till, and untilled fields. To estimate the fate of each bird from acute exposure, a random value is selected from the appropriate dose-response relationship and compared with the maximum retained dose. If acute exposure exceeds the randomly chosen effects value, mortality is assumed. For chronic risk, the most sensitive No Observed Adverse Effects Level (NOAEL) and Lowest Observed Adverse Effects Level (LOAEL) for an apical endpoint (survival, growth, reproduction) are compared with maximum rolling average total daily intake. In this article, we describe a case study conducted with the ESASeedPARAM for imidacloprid used as a seed treatment on wheat and soybean. Integr Environ Assess Manag 2023;19:527-546. © 2022 SETAC.
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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 | 0.000 | 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".