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Record W2288973480 · doi:10.1093/ee/nvw002

Seasonal Variability in Spider Assemblages in Traditional and Transgenic Rice Fields

2016· article· en· W2288973480 on OpenAlexaff
Sheng Lin, Liette Vasseur, Minsheng You

Bibliographic record

VenueEnvironmental Entomology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsBrock University
Fundersnot available
KeywordsSpiderBiologyAbundance (ecology)EcologyAssemblage (archaeology)Growing seasonPaddy fieldRelative species abundanceEcosystemSampling (signal processing)BiodiversityAgronomy

Abstract

fetched live from OpenAlex

The use of Bt transgenic rice (or Bt rice) remains controversial in several countries, including China. Risk assessments are a prerequisite to confirm the safety of Bt rice for ecosystems before a commercial release. This study was conducted to compare the responses of spider assemblages to Bt rice and nontransgenic rice. Two experiments with different locations and times were conducted, and the data were analyzed using standard diversity indices and multivariate community analysis. With both analytical approaches, spider diversity and assemblage composition were not significantly different between Bt and non transgenic rice fields. However, based on principal component analyses, temporal (seasonal) variations occurred in the composition of the spider assemblage. In this study, Bt rice had no detrimental effects on the spider assemblages, although assemblage composition and species abundance varied during the growing season. This study demonstrated an advantage in using community assemblages and repeated sampling to compare fields over a growing season because changes in the assemblages, and more specifically for some species, not always the most dominant, may vary over time. To more accurately assess the changes in composition and structure of spider assemblages through time, particularly for those species that may require a longer period to detect a response, an increase in sampling effort and longer-term experiments might be required.

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 score0.728
Threshold uncertainty score0.331

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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