Neonicotinoid insecticide residues in soil dust and associated parent soil in fields with a history of seed treatment use on crops in southwestern Ontario
Bibliographic record
Abstract
Using neonicotinoid insecticides as seed treatments is a common practice in field crop production. Exposure of nontarget organisms to neonicotinoids present in various environmental matrices is debated. In the present study, concentrations of neonicotinoid residues were measured in the top 5 cm of soil and overlying soil surface dust before planting in 25 commercial fields with a history of neonicotinoid seed treatment use in southwestern Ontario in 2013 and 2014 using liquid chromatography-electrospray ionization tandem mass spectrometry. The mean total concentrations were 3.05 ng/g and 47.84 ng/g in 2013 and 5.59 ng/g and 71.17 ng/g in 2014 for parent soil and soil surface dust, respectively. When surface and parent soil residues were compared the mean concentration in surface dust was 15.6-fold and 12.7-fold higher than that in parent soil in 2013 and 2014, respectively. Pooled over years, the surface dust to parent soil ratio was 13.7, with mean concentrations of 4.36 ng/g and 59.86 ng/g for parent soil and surface dust, respectively. The present study's results will contribute important knowledge about the role these residues may play in the overall risk assessment currently under way for the source, transport, and impact of neonicotinoid insecticide residues in a maize ecosystem.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".