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Record W1963841771 · doi:10.1603/ec13110

Insecticide Susceptibility of Three Species of Cutworm (Lepidoptera: Noctuidae) Pests of Grapes

2013· article· en· W1963841771 on OpenAlexaffabout
Michael J. Smirle, Cheryl L. Zurowski, D. Thomas Lowery, Ayman Mostafa

Bibliographic record

VenueJournal of Economic Entomology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCutwormBiologyNoctuidaeInstarPEST analysisLepidoptera genitaliaLarvaIntegrated pest managementToxicologyCropAgronomyBotany

Abstract

fetched live from OpenAlex

Climbing cutworms in the genus Abagrotis are economically important pests of grapes in the Okanagan Valley of British Columbia (BC). Grapes are recently introduced into many areas of the region, and the association between crop and pest is new and still evolving. This has led to limited information being available on pest management strategies, including the evaluation of chemical controls compatible with local production practices. Few insecticides are currently registered in Canada for cutworm control on grapes, and our study was initiated to provide information on the efficacy of potential control materials. We were also interested in the relative susceptibilities of the three most common cutworm species attacking grape buds in BC--Abagrotis orbis (Grote), Abagrotis reedi Buckett, and Abagrotis nefascia (Smith). Dose-response bioassays with nine insecticides were conducted on neonate larvae using Bok Choy leaf disks, and on fourth-instar larvae using diet incorporation. There were considerable differences in the toxicity of insecticides within species for neonates and fourth instars. For some materials, the relative toxicity to neonates and fourth instars were not correlated. Response to insecticides among the three species showed variation as well, and correct identification of the species complex present in individual locations is important in choosing the best available control material.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0070.000

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 head0.040
GPT teacher head0.265
Teacher spread0.225 · 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.

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

Citations5
Published2013
Admission routes2
Has abstractyes

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