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Record W4396917831 · doi:10.1093/ee/nvae041

Characteristics of logs with signs of oviposition by the polyphagous xylophage Asian longhorned beetle (Coleoptera: Cerambycidae)

2024· article· en· W4396917831 on OpenAlexaffabout
Jean Turgeon, John Pedlar, Ronald E. Fournier, Michael T. Smith, Mary Orr, Ben Gasman

Bibliographic record

VenueEnvironmental Entomology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Food Inspection AgencyCanadian Forest Service
Fundersnot available
KeywordsLonghorn beetleBiologyInfestationDiameter at breast heightHorticultureBasal areaBotanyForestryEcologyGeography

Abstract

fetched live from OpenAlex

During the eradication program undertaken against Anoplophora glabripennis (Motschulsky) in the Greater Toronto Area, information was collected on the numerous signs of injury found on wounded trees. Herein, we used a portion of this information to assess the characteristics of logs with signs of oviposition (i.e., pits). Specifically, we related the basal diameter, type (log bole vs. log branch), height above ground, and branch hierarchy level of logs with pits to tree size (i.e., height and diameter at breast height) and level of infestation intensity. In general, pits were concentrated on logs from the bole and branches that were 8-14 cm in diameter in the lower 8 m of the bole and in the first 2 levels of the branching hierarchy. Oviposition pit location was strongly influenced by tree size-both height and diameter at breast height, with more pits on the lower bole in small trees and then higher on the bole and into the branches as tree size increased. As tree-level infestation intensity increased, pits were found on both larger and smaller diameter portions of the trees, presumably as preferred oviposition sites became saturated. These findings can improve the efficacy of surveillance activities for A. glabripennis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.191
Teacher spread0.187 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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