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Record W1986418854 · doi:10.4039/tce.2014.58

Semiochemistry and chemical ecology of the emerald ash borer<i>Agrilus planipennis</i>(Coleoptera: Buprestidae)

2014· article· en· W1986418854 on OpenAlexaffabout
Peter J. Silk, Krista Ryall

Bibliographic record

VenueThe Canadian Entomologist · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsBuprestidaeAgrilusEmerald ash borerFraxinusKairomoneChemical ecologyEcologyBiologyHost (biology)

Abstract

fetched live from OpenAlex

Abstract The emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae) is a very serious invasive pest in North America, causing extremely high levels of mortality to ash trees ( Fraxinus Linnaeus, Oleaceae) in the United States of America and Canada. Knowledge of buprestid chemical ecology is sparse, but the appearance of EAB in North America and its devastating ecological and economic impacts, particularly in the urban environment, have provided an opportunity to study the semiochemistry, natural history, and ecology of this buprestid in detail. This review will summarise the chemical ecology of EAB to date, discussing studies on semiochemistry, natural history, and behaviour with respect to host and mate finding that have identified several female-produced pheromone components (contact and sex pheromones), and attractive host kairomones. Earlier reviews focused on studies of attractive host volatiles with respect to development of a trapping system and visual and contact phenomena in EAB mate finding. This has led to the development of an efficient trapping system for EAB, with attempts to optimise the range of variables in trap protocols, combining pheromone components, release rates, and combinations with host kairomones, as well as trap type, placement, height, and colour being taken into account.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.862

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)0.0010.000

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 head0.181
Teacher spread0.176 · 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

Citations26
Published2014
Admission routes2
Has abstractyes

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