Quantifying Neonicotinoid Insecticide Residues Escaping during Maize Planting with Vacuum Planters
Bibliographic record
Abstract
Neonicotinoid residues escaping in vacuum-planter exhaust during maize planting were measured in 25 fields in southwestern Ontario in 2013-2014 using filter bags to collect planter exhaust dust and horizontal and vertical sticky traps to collect planter operation-generated dust. Atrazine residues were used to differentiate between neonicotinoid residues originating from seed or from disturbed soil. Recovery rates of seed-applied neonicotinoids in exhaust were 0.014 and 0.365% in 2013 and 2014, respectively, calculated on the basis of neonicotinoid concentrations in preplant soil and seed application rates. Neonicotinoid exhaust emission rates were 0.0036 and 0.1104 g/ha for 2013 and 2014, respectively, with 99.9472 and 99.7820% originating from treated seed in 2013 and 2014, respectively, calculated on the basis of the atrazine marker. Rates of recovery of seed-applied neonicotinoid residues by exhaust filter bags were 0.015 and 0.437% for 2013 and 2014, respectively. Neonicotinoid residues captured on horizontal and vertical traps were 1.10 ng/cm2 (0.1104 g/ha) and 1.45 ng/cm2 (0.0029 g/ha), respectively, with 92.31 and 93.03% originating from treated seed, respectively, representing 0.3896% of the original active ingredient applied to the seed planted. Exposure to pollinators can be best reduced by strategies to keep active ingredient on the seed, below the soil surface, and in the field where applied.
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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".