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Record W2039702542 · doi:10.1080/15459620701386293

Pesticide Aerosol Characteristics in the Vicinity of an Agricultural Vehicle Cab During Application

2007· article· en· W2039702542 on OpenAlexaff
Denis Bémer, Joelle Fismes, I. Subra, V. Blachère, J. C. Protois

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsInstitut National de la Recherche Scientifique
FundersCaisse Centrale de la Mutualité Sociale AgricoleMutualité Sociale Agricole
KeywordsAerosolPesticideEnvironmental scienceMass concentration (chemistry)TRACERChemistryEnvironmental chemistryEnvironmental engineeringPhysicsAgronomy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.076

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.217
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2007
Admission routes1
Has abstractyes

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