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Record W2907690343 · doi:10.5539/jas.v11n2p201

Influence of Bt Maize on Diversity and Composition of Non-target Arthropod Species

2019· article· en· W2907690343 on OpenAlexvenueno aff
Maria Freire de Sousa, Marcos Gino Fernandes, Anderson José da Silva Guimarães

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBiologyBacillus thuringiensisArthropodCultivarCropGenetically modified cropsAgronomyComposition (language)TransgeneBotanyEcologyGeneGenetics

Abstract

fetched live from OpenAlex

Non-target arthropods may be affected by toxins derived from Bacillus thuringiensis (Bt) expressed in transgenic maize. The objective of this study was to evaluate the possible impacts of Bt maize on the diversity and the composition of non-target arthropod species by analyzing one field cultivated with conventional maize (no expressing transgenic protein) and three fields cultivated with transgenic maize (expressing Bt proteins). In each field was sampled 50 entirely plants for the number of arthropod specimens and registred the degree of injury caused by the chewing insects. A total of 2.525 specimens of arthropods, comprising 29 species from 25 families, were recorded on 3.000 sampled plants. The most diverse family belonged to the order Hemiptera. Based on Shannon and Simpson indexes, the Bt-transgenic cultivar EXP3320YG had lower level of non-target arthropod diversity than other cultivars. From this study, it is clear that the diversity of non-target arthropods on maize crop is negatively affected by Cry1Ab protein, while the Cry1A105+Cry2Ab2+Cry1F proteins, and Cry1A105+Cry2Ab2+Cry3Bb1 proteins do not have any effect on arthropod species diversity and composition.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.131

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 head0.004
GPT teacher head0.204
Teacher spread0.200 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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