The role of field dust in pesticide drift when pesticide‐treated maize seeds are planted with vacuum‐type planters
Bibliographic record
Abstract
Abstract BACKGROUND Neonicotinoid‐contaminated dust escaping pneumatic seeders causes exposure to non‐target organisms such as pollinators. Two sources of dust have been reported: abrasion by talc which is added as seed lubricant during planting, and seed‐to‐seed abrasion occurring during seed handling, distribution and planting. We report a third important source that warrants remediation. Here, soil dust stirred up by planters was found to enter the vacuum air intake near seed metering devices. RESULTS The mean quantity of dust collected from the exhaust of a commercial pneumatic planter over a number of field sites and situations was 46 g ha−1, ranging from 5.8 to 184.2 g ha−1. While the clothianidin concentration in exhaust dust declined with increasing quantity of dust, total clothianidin recovered increased linearly within the study parameters. Up to 2.4 g ha−1 of clothianidin was recovered from planter exhaust, representing approximately 12.6% of the active ingredient applied to seed. A similar pattern occurred in the laboratory on a single standing planter unit using diatomaceous earth as surrogate field dust. CONCLUSION Field dust in pneumatic metering systems contributes significantly to clothianidin contamination in planter exhaust by seed abrasion. Adding diatomaceous earth as surrogate field dust to the Heubach seed dust protocol accounted for field dust abrasion and distinguished anti‐abrasive properties of seed treatments. © 2017 Society of Chemical Industry
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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 | 0.000 | 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) | 0.001 | 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".