Effects of neonicotinoid seed treatments on wild bee populations in soybean and corn fields in eastern Ontario
Bibliographic record
Abstract
Abstract Neonicotinoid‐coated corn and soybean seeds are a common crop in Canada and the US. A growing body of research is demonstrating that, through various exposure routes, neonicotinoids can impact a suite of nontarget organisms including beneficial insects such as bees. However, to date, only a few studies have examined the effects of neonicotinoids in field settings. We assessed the relationship between agricultural crop soil neonicotinoid levels and wild bee abundance and diversity at 16 agricultural sites representing different soil neonicotinoid levels. We detected clothianidin at 11 sites, thiamethoxam at three sites; imidacloprid was not detected. Hedgerow and crop soils were consistent in terms of where clothianidin was detected; thiamethoxan was not detected in hedgerow soils. Based on model outcomes, fields with higher levels of soil neonicotinoids exhibited significantly lower wild bee abundance and diversity than those with low or no neonicotinoids detected. Crop soil neonicotinoid level, hedgerow floral resource abundance and crop type were consistent predictors of bee abundance across models; only neonicotinoid level and crop type were significant predictors of diversity. Our results are consistent with recent findings in the midwestern US, and underscore the potential risk of soil neonicotinoids to wild bee populations across regions and crop systems.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".