MétaCan
Menu
Back to cohort
Record W3193522499 · doi:10.3390/insects12080750

Biodiversity in a Cool-Climate Vineyard: A Case Study from Quebec

2021· article· en· W3193522499 on OpenAlexafffundabout
Charles Vincent, Jacques Lasnier, Charles-Henri de Coussergues, Alain Baril

Bibliographic record

VenueInsects · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsResearch & Development CorporationAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaMcGill University
KeywordsBiologyBiodiversityVineyardPopilliaScarabaeidaeEcologyAgroforestryViticultureJapanese beetleHorticulture

Abstract

fetched live from OpenAlex

In Quebec (Canada), viticulture has experienced steady growth in the last 35 years in terms of surfaces cultivated and value, although it is practiced in climatic conditions at the edge of what is considered a cool-climate area. This case study documents biodiversity studies conducted at the l’Orpailleur vineyard (Dunham, QC, Canada) from 1997 to 2021. In a first phase starting in 1997, the biodiversity of insecticide-free and insecticide-treated plots was determined for the taxa Scarabaeidae, Curculionidae, Chrysomelidae, Cicadellidae, Acari and Aranae. This step provided a baseline allowing to identify key arthropods. In a second phase starting in 2004, entomological issues were addressed on an ad hoc basis. In 2014, a third phase began with a perspective of sustainability and management of plant diversity in the vineyard to conserve natural enemies. Because of increased Japanese beetle (Popillia japonica-Scarabaeidae) populations and threats to vineyards, a biocontrol program based on the parasitoid Istocheta aldrichi (Tachinidae) was initiated. The unusually fast development of grapevines during the growing season, selection of flowering species, as well as selected arthropods associated with these flowering species, will be illustrated. Periodic update of protection programs will be required to address future challenges associated with climate change scenarios and world trade.

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 score0.543
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

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

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 head0.050
GPT teacher head0.272
Teacher spread0.222 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueInsectsSame topicHorticultural and Viticultural ResearchFrench-language works237,207