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Record W47974227 · doi:10.48044/jauf.2004.040

Relative Susceptibility of Woody Landscape Plants to Japanese Beetle<i>(Coleoptera: Scarabaeidae)</i>

2004· article· en· W47974227 on OpenAlexaboutno aff
David W. Held

Bibliographic record

VenueArboriculture & Urban Forestry · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersMississippi State University
KeywordsPopilliaJapanese beetleScarabaeidaeBiologyResistance (ecology)JaponicaTiliaBotanyLeaf beetleWoody plantOrnamental plantAgronomyPollenLarva

Abstract

fetched live from OpenAlex

Abstract The Japanese beetle (Popillia japonicaNewman) was introduced to a New Jersey nursery in 1916 and continues to spread across the United States and Canada. Adults attack foliage, flowers, and fruit of more than 300 species of plants; however, some plants are notably resistant. This paper summarizes data on plant susceptibility of woody plants to Japanese beetles collected from observations and controlled experiments. Resistance to Japanese beetle has been documented among species of maples (Acer) and birch (Betula) and among cultivars of crabapple (Malus), crapemyrtle (Lagerstroemia), and linden (Tilia). Production of certain plant odors, presence of secondary compounds in leaves, and leaf pubescence are factors affecting resistance to this insect. Host plant resistance is the most sustainable means of managing feeding damage or plant losses resulting from Japanese beetle adults. When suitable, incorporating Japanese beetle–resistant plants into new landscapes can reduce or eliminate the expense of replacing damaged plants or frequent insecticide applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2004
Admission routes1
Has abstractyes

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