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Record W2115510472 · doi:10.4039/n09-017

Effects of crop rotation and genetically modified herbicide-tolerant corn on ground beetle diversity, community structure, and activity density

2010· article· en· W2115510472 on OpenAlexafffund
Stéphane Bourassa, Héctor A. Cárcamo, John R. Spence, Robert E. Blackshaw, Kevin D. Floate

Bibliographic record

VenueThe Canadian Entomologist · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaWeedBiologyAgronomyGround beetleCrop rotationBrassicaCropRotation systemEcologyHabitatNitrogenChemistry

Abstract

fetched live from OpenAlex

Abstract Ground beetles (Coleoptera: Carabidae) were sampled in conventional and genetically modified herbicide-tolerant (GMHT) corn, Zea mays L. (Poaceae), planted under rotation with canola, Brassica L. (Brassicaceae), or continuously cropped corn to investigate the influence of corn variety and rotation on the structure of carabid assemblages. Corn variety, cultivation regime, and their interaction all influenced overall carabid activity density. Weed management associated with corn variety influenced the activity density of a few carabid species and this was attributed to changes in vegetation. Some smaller bodied carabids such as Bembidion quadrimaculatum L. were less abundant in GMHT plots, probably because weed density was higher in midseason, but the opposite was observed for larger bodied carabids such as Pterostichus melanarius (Illiger). Overall, rotating corn with canola had a stronger effect on carabid community structure than did corn variety. We suggest that GMHT corn has little impact on the overall carabid fauna but may influence the activity of certain species through effects on the weed community.

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 score0.966
Threshold uncertainty score0.693

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 studies0.0010.000
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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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