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Record W2121477160 · doi:10.4039/tce.2012.53

Diversity and seasonal density of carabid beetles (Coleoptera: Carabidae) in relation to the soybean aphid in soybean crop in Québec, Canada

2012· article· en· W2121477160 on OpenAlexafffundabout
Annabelle Firlej, Annie‐Ève Gagnon, Simon Laurin-Lemay, Jacques Brodeur

Bibliographic record

VenueThe Canadian Entomologist · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversité LavalUniversité de Montréal
FundersCanada Research Chairs
KeywordsSoybean aphidOverwinteringBiologyAphididaeAphidHemipteraSpecies richnessAgronomyCropEcologyPEST analysisGrowing seasonBotanyHomoptera

Abstract

fetched live from OpenAlex

Abstract We studied the diversity and summer seasonal activity-density of Carabidae associated with soybean fields infested by the soybean aphid (Aphis glycinesMatsumura; Hemiptera: Aphididae) in Québec, Canada. Carabid beetles were sampled in six to seven fields from June to September 2004 and 2005 using pitfall traps. A total of 33 species from 15 genera were identified, with the exoticPterostichus melanarius(Illiger) (Coleoptera: Carabidae), representing 75.8% and 84.5% of all individuals trapped in 2004 and 2005, respectively. Large variations in species richness and diversity indexes were observed between fields within and between years. Multivariate analyses showed that carabid activity-density varied as a function of field location and sampling period, with individuals belonging to species overwintering as adults being more abundant early in the growing season. There was no relationship between carabid trap catches andA. glycinesdensity, suggesting that carabid beetles do not respond numerically to soybean aphid populations at the spatial scale studied.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.197
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2012
Admission routes3
Has abstractyes

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