Pesticide Aerosol Characteristics in the Vicinity of an Agricultural Vehicle Cab During Application
Bibliographic record
Abstract
Pesticide spraying for crop protection leads to the formation of a mist of droplets, part of which is dispersed into the atmosphere. The characteristics of this aerosol, namely its particle size distribution and concentration, were measured during five campaigns involving cereal crop growing, wine grape culture, and orcharding. The measurement method incorporated a tracer product (fluorescein) with the treatment product; the pesticide aerosol concentration was then deduced from the tracer concentration. This method was validated by comparing the pesticide concentration determined by tracing with the concentration determined by direct measurement of the active substance of the pesticide. Concentration was measured using sampling filters, and particle size distribution was measured using cascade impactors. Instruments were mounted on an agricultural vehicle cab to optimize aerosol characterization, and then the cab's confinement efficiency was determined. Aerosols analyzed were fine, featuring mass median diameters between 4 microm and 15 microm; they are therefore highly dispersive. Their concentration is sufficiently high to justify operator protection by an efficient, filtered-air, pressurized cab, especially in wine grape culture and orcharding, which are the sectors where the highest pesticide transfers have been observed.
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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".