A global nectar and pollen pesticide residue database with a user interface tool for calculating residue per unit dose for different pesticide application methods
Bibliographic record
Abstract
Pollinating bee dietary risk assessment for pesticide registration requires knowledge of nectar and pollen pesticide residue concentrations following different pesticide application methods to crops. The magnitude and duration of bee dietary pesticide exposures vary according to crop attractiveness to bees, physiochemical properties, plant characteristics, application rate, method, timing, and soil characteristics. Regulatory authorities rely on model-generated default estimates of pollinator exposure when measured food item pesticide residue data are unavailable for pesticide active ingredients. In North America, default pesticide residue estimates for pollen and nectar are imbedded in the USEPA's BeeREX model and, depending on the application method, are derived from various model approaches and data sources. Pursuing comprehensive bee-relevant data, we compiled and analyzed pesticide residue data from nectar and pollen samples collected during numerous field studies previously submitted to the USEPA, California Department of Pesticide Regulation, Canada Pesticide Regulatory Agency, and the European Food Safety Authority by pesticide product registrants. The information was compiled into a database that is accessible through an interactive Excel user interface termed NPRUDv1. The interactive file that makes up NPRUDv1 allows the user to generate statistical estimates of pesticide residue per unit dose values in nectar and pollen matrices for different application methods. The values can be used to calculate nectar and pollen estimated environmental concentrations in models to assess dietary pollinator risk. The use of this database and the NPRUDv1 tool will strengthen the dietary exposure component of pollinator pesticide risk assessments by utilizing a database of field-measured pollen and nectar residue concentrations that represent pesticide use patterns in different crops. This publication describes the procedures followed to establish a globally comprehensive nectar and pollen residue database, demonstrates the use of NPRUDv1, and demonstrates its applicability to lower tier pollinator pesticide risk assessment.
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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".