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Record W2079228794 · doi:10.2478/jas-2014-0021

Bioassay for Detection of Dichlorvos Insecticide in Air in Alfalfa Leafcutting Bee (Megachile Rotundata F.) Incubators

2014· article· en· W2079228794 on OpenAlexafffund
John Purdy, Peter G. Kevan

Bibliographic record

VenueJournal of Apicultural Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDichlorvosBiologyBioassayToxicologyChromatographyPesticideEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract Dichlorvos is an insecticide used in slow-release plastic strips for controlling chalcid wasp parasites, such as Pteromalus venustus Walker, in incubators used to raise alfalfa leafcutting bees (Megachile rotundata F.). Beekeepers need a practical method to detect dichlorvos in air and verify that it has dissipated to levels acceptable for worker re-entry and for the bees to emerge. We evaluated three methods for analysis of the dichlorvos concentration in air. Vapor sampling tubes using a manually operated pump or diffusion collection had insufficient sensitivity in the concentration range of interest. Air samples collected using battery powered pumps were analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS), which was accurate and sensitive, but too costly and slow for practical use. Finally, a convenient bioassay for detecting dichlorvos in air was developed using leafcutting bees and verified by comparison with the results obtained by LC/MS/MS for a series of dose levels. The bioassay is simple enough to be done by the beekeeper on-site, is inexpensive, and gives results within 1 h. The LC 50 for dichlorvos vapor in air after 1 h of exposure was 273.2 μg/m 3 by the probit regression method or 277.3 μg/m 3 by the logit regression method.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes2
Has abstractyes

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