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Record W2141193569 · doi:10.5539/jps.v4n1p21

Distribution of Pests on Vitis riparia in Sandy Soils of the South-Western Ontario

2014· article· en· W2141193569 on OpenAlexafffundvenueabout
Alireza Rahemi, Adam Dale, Helen Fisher, John Kelly, Toktam Taghavi, C. A. Singleton, Adam Bonnycastle

Bibliographic record

VenueJournal of Plant Studies · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsPetro-CanadaUniversity of Guelph
FundersAgriculture and Agri-Food CanadaAgricultural Adaptation Council
KeywordsPhylloxeraMidgeGallBiologyGeographyForestryAgronomyHorticultureEcologyBotanyRootstock

Abstract

fetched live from OpenAlex

Vitis riparia (Michaux) is native to North America and tolerates the local weather and soil conditions of south-western Ontario in Canada. A survey was done on V. riparia to elicit the distribution of pests on the species in central south-western Ontario. Eight hundred and forty four genotypes of V. riparia were observed throughout the sandy soils of five counties in Ontario (Brant, Elgin, Middlesex, Norfolk and Oxford). The location of the selected vines was labeled in the Geographic Information System (GIS). The ArcGIS program was used to make maps of the distribution of the wild grape pests, Phylloxera, Japanese beetle, Filbert gallmaker, Cane Filbert gallmaker, Tumid Filbert gallmaker and Tube Filbert gallmaker midges in those areas. The results show that the density of pests on V. riparia is more severe in some areas than others. Phylloxera and Japanese beetle were the major pests observed. Phylloxera was most prevalent and Japanese beetle least prevalent in Elgin County. The gallmaker midges were found in low densities throughout the area. The distribution of Phylloxera could be related to the soil type, and the distribution of Japanese beetle and Tumid gall midge to the land use. With Phylloxera, it appears that they prefer poor drainage soils which do not dry out readily. Whereas, Japanese beetle and Tumid gall midge prefer undisturbed soils where they can overwinter successfully.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.959

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 studies0.0000.000
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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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

Citations0
Published2014
Admission routes4
Has abstractyes

Explore more

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