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Record W2206263116 · doi:10.4039/tce.2015.68

Estimates of emerald ash borer (Coleoptera: Buprestidae) larval galleries in branch samples from asymptomatic urban ash trees (Oleaceae)

2015· article· en· W2206263116 on OpenAlexafffund
Jean Turgeon, Jeffrey G. Fidgen, Krista Ryall, Taylor Scarr

Bibliographic record

VenueThe Canadian Entomologist · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsMinistry of Natural Resources and ForestryNatural Resources CanadaCanadian Forest Service
FundersNatural Resources CanadaOntario Ministry of Natural Resources and ForestryUniversity of Pittsburgh
KeywordsEmerald ash borerBuprestidaeAgrilusOleaceaeFraxinusCrown (dentistry)ForestryBotanyAbundance (ecology)BiologyHorticultureEcologyGeography

Abstract

fetched live from OpenAlex

Abstract Agrilus planipennis (Coleoptera: Buprestidae), is causing extensive mortality of ash ( Fraxinus Linnaeus; Oleaceae) in North America. Once detected in an area, resource managers require methods to obtain estimates that could improve management decisions. We studied the within-crown and within-branch distribution and abundance of A. planipennis feeding galleries by sampling 3-m-long branches from asymptomatic urban ash trees and subdividing each branch into 12 sections of 25 cm each. We found galleries in all 12 sections of some, but not all, branches. Section was a significant source of variation in A. planipennis gallery density/m 2 of branch surface area. A comparison of predictive power and efficiency of estimates for samples of increasing length, and for samples of the same length but consisting of different combinations of sections, revealed that those based on the two basal 25-cm sections of a branch from the lower-crown or mid-crown of an asymptomatic tree were less accurate and precise than those based on more sections, but were the most cost effective. Whittling more sections per branch, irrespective of the combinations of branch sections per length, improved predictive power but reduced cost effectiveness. We also observed that crown level was not important, and aspect was only marginally so, when estimating gallery abundance per sampled branch.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.232
Teacher spread0.208 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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