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Record W163878347 · doi:10.4137/aswr.s2994

Atmospheric Concentrations of Captan and Folpet in the Lower Fraser Valley Agricultural Region of Canada

2009· article· en· W163878347 on OpenAlexaffabout
Renata Raina, Wayne Belzer, Keith Jones

Bibliographic record

VenueAir Soil and Water Research · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Regina
FundersNational Cancer Institute
KeywordsCaptanAtmosphere (unit)Environmental scienceAgricultureGeographyHydrology (agriculture)FungicideMeteorologyHorticultureBiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

Two N-trihalomethylthio fungicides were detected in the atmosphere in the Lower Fraser Valley agricultural region of Canada. Captan was detected in both the particle and gas phase with a dominant particle phase fraction observed in both 2005 and 2006 (only total captan atmospheric concentrations were available for 2004). This provides the first evidence of particle transport as a significant atmospheric transport pathway for captan in an agricultural region in Canada. Weekly captan air concentrations reached maximum levels of 13.2 ng m -3 in June 2006, while for folpet total atmospheric levels were lower with maximum reaching 1.7 ng m -3 in August 2004 and generally <1 ng m 3 in 2005 and 2006. Folpet is detected in the atmosphere although not previously reported in usage inventories. In the three years examined (2004-2006) captan concentrations observed a seasonal maximum in atmospheric concentrations during spring to early summer coinciding with expected peak usage period on crops in the Lower Fraser Valley agricultural region located in British Columbia, Canada. No usage data is available in Canada beyond 2003 but these seasonal trends show that captan remains a dominant pesticide used in this agricultural region with no decline in atmospheric concentrations during 2004-2006.

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 score0.404
Threshold uncertainty score0.849

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 head0.025
GPT teacher head0.248
Teacher spread0.223 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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