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Entomological Opportunities and Challenges for Sustainable Viticulture in a Global Market

2018· review· en· W2621066893 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Entomology · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVineyardIntegrated pest managementViticultureBiologyEcosystem servicesPEST analysisPest controlAgroforestrySustainabilityEcologyEcosystemBotany

Abstract

fetched live from OpenAlex

Viticulture has experienced dramatic global growth in acreage and value. As the international exchange of goods has increased, so too has the market demand for sustainably produced products. Both elements redefine the entomological challenges posed to viticulture and have stimulated significant advances in arthropod pest control programs. Vineyard managers on all continents are increasingly combating invasive species, resulting in the adoption of novel insecticides, semiochemicals, and molecular tools to support sustainable viticulture. At the local level, vineyard management practices consider factors such as the surrounding natural ecosystem, risk to fish populations, and air quality. Coordinated multinational responses to pest invasion have been highly effective and have, for example, resulted in eradication of the moth Lobesia botrana from California vineyards, a pest found in 2009 and eradicated by 2016. At the global level, the shared pests and solutions for their suppression will play an increasing role in delivering internationally sensitive pest management programs that respond to invasive pests, climate change, novel vector and pathogen relationships, and pesticide restrictions.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.063
GPT teacher head0.324
Teacher spread0.261 · 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