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Record W2565069696 · doi:10.4039/tce.2016.42

Hitching a ride: firewood as a potential pathway for range expansion of an exotic beech leaf-mining weevil, <i>Orchestes fagi</i> (Coleoptera: Curculionidae)

2016· article· en· W2565069696 on OpenAlexaffabout
Andrew Morrison, Jon Sweeney, Cory Hughes, Rob Johns

Bibliographic record

VenueThe Canadian Entomologist · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceForest Protection Limited (Canada)University of New Brunswick
Fundersnot available
KeywordsBeechCurculionidaeWeevilFirewoodBotanyRange (aeronautics)BiologyEcology

Abstract

fetched live from OpenAlex

Abstract We investigated the potential for human-mediated range expansion of an exotic beech leaf-mining weevil, Orchestes fagi (Linnaeus) (Coleoptera: Curculionidae: Curculioninae: Rhamphini) (formerly known as Rhynchaenus fagi ) on timber or firewood, which for eight to nine months of the year may harbour adults in diapause. In both relatively low-density and high-density populations, adults were found on the base, middle, and upper boles of the primary host, American beech ( Fagus grandifolia Ehrhart; Fagaceae), as well as red maple ( Acer rubrum Linnaeus; Sapindaceae) and red spruce ( Picea rubens Sargent; Pinaceae) in the vicinity. Comparatively few individuals were found on tree branches, or in the moss, duff, or soil collected beneath beech trees. Overwintering adults appeared to favour parts of trees with relatively high bark roughness. Our study suggests that, between the months of July through May, any woody stems near areas having O. fagi outbreaks are likely to harbour adults. Moreover, as all of the trees studied are common sources of timber or firewood, the harvest and transport of wood from these areas may facilitate outbreak spread; this may explain the multiple, distantly distributed populations of O. fagi that have been reported in eastern Nova Scotia, Canada in recent years.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.231
Teacher spread0.211 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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