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Record W2153410374 · doi:10.1093/ee/nvu061

Striped Cucumber Beetle (Coleoptera: Chrysomelidae) Aggregation in Response to Cultivar and Flowering

2015· article· en· W2153410374 on OpenAlexaboutno aff
Jeffrey Gardner, Michael Hoffmann, Michael Mazourek

Bibliographic record

VenueEnvironmental Entomology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersCornell University Agricultural Experiment StationNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsCultivarBiologyPEST analysisAgronomyIntegrated pest managementPhenologyTrap cropCropBrassica oleraceaHorticulture

Abstract

fetched live from OpenAlex

The striped cucumber beetle [Acalymma vittatum (F.)] is a specialist pest of cucurbits throughout its range in the United States and Canada. Improved integrated pest management options are needed across the pest's range, especially on organic farms where there are few effective controls. Trap cropping in cucurbits is an option, but there are significant challenges to the technique. Because cucurbit flowers are highly attractive to the beetles, four field experiments tested whether cultivar and phenology interact to preferentially aggregate beetles. The first experiment tested the hypothesis that cucurbit flowers were more attractive to striped cucumber beetles than was foliage. The second experiment tested whether there were differences in beetle aggregation between two relatively attractive cultivars. The third and fourth experiments were factorial designs with two plant cultivars and two levels of flowering to specifically test for an interaction of cultivar and flowering. Results indicated that flowers were more attractive than foliage, beetle aggregation was affected by plant cultivar, and that there was an interaction of cultivar with flowering. We conclude that a single cultivar may be sufficient to serve as a generic trap crop to protect a wide variety of cucurbits.

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 score0.881
Threshold uncertainty score0.353

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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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