222c - Human biomonitoring of neonicotinoid insecticides in environmental and occupational settings
Bibliographic record
Abstract
Abstract Neonicotinoid insecticides and neonicotinoid-like compounds (NNIs) are the most widely used insecticides in the world, accounting for over 25% of the insecticide market. In recent years, NNIs have been identified as potential hazards to humans and are included in chemical priority lists in the European Union, the United States, and Canada. However, NNIs such as acetamiprid and flupyradifurone, are still widely used as plant protection products throughout the EU. Additionally, imidacloprid is still used as a flea treatment for cats, dogs and other pets. Human biomonitoring allows for a quantitative assessment of internal chemical exposures by analysing biological media. Despite growing concern about the hazardous properties of NNIs, there have only been a few human biomonitoring (HBM) studies investigating NNI exposures conducted in the EU and none in Ireland. The EIRE ‘nEonicotinoid Insecticide exposuREs’ project is a human biomonitoring study investigating exposures to NNIs among the general population and occupational users of NNIs in Ireland. Urine samples from the general Irish population (n=227) were analysed for seven major NNIs using LC-MS/MS Of those samples, 76% had quantifiable levels of at least one NNI, indicating a potential for widespread exposure of NNIs among the Irish population. An occupational study of professional gardeners and pet shelter workers who use NNIs will commence in 2024. Preliminary results from the general Irish population study and the recruitment strategy for the occupational studies of professional gardeners and pet shelter workers will be presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".