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Record W4240996058 · doi:10.1673/031.014.73

Diversity and Abundance of Leafhoppers in Canadian Vineyards

2014· article· en· W4240996058 on OpenAlexaffabout
Julien Saguez, Chrystel Olivier, Andrew J. Hamilton, L. W. Stobbs, Jacques Lasnier, Brian Galka, Xiang‐Sheng Chen, Yves Mauffette, Charles Vincent

Bibliographic record

VenueJournal of Insect Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsUniversité du Québec à MontréalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyHemipteraLeafhopperAbundance (ecology)AuchenorrhynchaSpecies richnessTemperate climateCicadomorphaPEST analysisEcologyBotanyHomoptera

Abstract

fetched live from OpenAlex

Leafhoppers (Hemiptera: Cicadellidae) are pests of many temperate crops, including grapevines (Vitis species). Uncontrolled populations can induce direct and indirect damage to crops due to feeding that results in significant yield losses and increased mortality in infected vineyards due to virus, bacteria, or phytoplasmas vectored by leafhoppers. The main objective of this work was to determine the diversity of leafhoppers found in vineyards of the three main Canadian production provinces, i.e., in British Columbia, Ontario, and Quebec. Approximately 18,000 specimens were collected in 80 commercial vineyards from 2006 to 2008. We identified 54 genera and at least 110 different species associated with vineyards, among which 22 were predominant and represented more than 91% of all the leafhoppers. Species richness and diversity were estimated by both Shannon's and Pielou's indices. For each province, results indicated a temporal variation in species composition. Color photographs provide a tool to quickly identify 72 leafhoppers commonly associated with vineyards.

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)0.001
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 score0.027
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.198
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

Citations13
Published2014
Admission routes2
Has abstractyes

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