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Record W3213223093 · doi:10.23986/afsci.108983

Resistance developments in Estonia’s population of pollen beetles (Brassicogethes aeneus)

2021· article· en· W3213223093 on OpenAlexfundno aff
Liina Kann, Mati Koppel, Tanel Kaart, Bulat Islamov, Pille Sooväli, Andres Mäe

Bibliographic record

VenueAgricultural and Food Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
FundersMinistry of Rural Affairs
KeywordsThiaclopridBiologyChlorpyrifosCyhalothrinPollenPopulationToxicologyPEST analysisBotanyPesticideAgronomyThiamethoxamImidaclopridDemography

Abstract

fetched live from OpenAlex

A total of 111 local pollen beetle populations were collected from both winter and spring oilseed rape fields, in the main oilseed growing regions of Estonia between 2015−2019. The objective was to analyse the insecticide-susceptibility of the pollen beetle population (in the form of Brassicogethes aeneus). The pollen beetle samples were tested for sensitivity to lambda-cyhalothrin, thiacloprid, and chlorpyrifos. The efficacy of the tested insecticides varied considerably by region. We observed a clear decrease in susceptibility to lambda-cyhalothrin and thiacloprid, but sensitivity to chlorpyrifos remained stable throughout the period between 2015 and 2019. Amongst the tested samples in that period, a total of 3% were classified as susceptible to lambda-cyhalothrin, 18% as moderately resistant, 70% as resistant, and 7% as highly resistant. In the case of thiacloprid, 21% of the samples were highly susceptible to the insecticide, 39% were susceptible, and 41% had reduced levels of susceptibility to the insecticide. The information which was presented tended to confirm the ongoing evolution of insecticide resistance in the B. aeneus population in Estonia, while also highlighting the importance of data-based decisions when optimising insecticide resistance management in the field.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.242
Teacher spread0.217 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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