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Record W2137461835 · doi:10.4039/n09-064

Influence of epicuticular-wax composition on the feeding pattern of a phytophagous insect: implications for host resistance

2010· article· en· W2137461835 on OpenAlexaffabout
Simon P. Daoust, Brian J. Mader, Éric Bauce, Emma Despland, Audrey Dussutour, P. J. Albert

Bibliographic record

VenueThe Canadian Entomologist · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversité LavalConcordia UniversityUniversité de Montréal
FundersU.S. Forest Service
KeywordsSpruce budwormChoristoneura fumiferanaTortricidaeLepidoptera genitaliaBiologyWaxEpicuticular waxPinaceaeBotanyLarvaInsectHost (biology)Resistance (ecology)HorticultureAgronomyEcologyPinus <genus>

Abstract

fetched live from OpenAlex

Abstract A white spruce, Picea glauca (Moench) Voss (Pinaceae), plantation in southern Quebec was found to contain two distinct types of trees, the first resistant and the second susceptible to attack by spruce budworm, Choristoneura fumiferana (Clemens) (Lepidoptera: Tortricidae). To identify the mechanisms of white spruce resistance to spruce budworm, we studied the role of epicuticular waxes, comparing ( i ) the foliar chemistry of susceptible and resistant trees and ( ii ) the feeding pattern of larvae at first contact with the foliage. Needles collected from resistant trees contained concentrations of the monoterpenes α-pinene and myrcene that were 307% and 476%, respectively, above those found in needles collected from susceptible trees. Although there were no significant differences in probing behaviour, significantly fewer larvae transitioned from probing to feeding on resistant needles; this led to fewer feeding bouts as well as a significantly shorter first meal. Removal of waxes increased the number of individuals transitioning from probing to feeding on resistant needles; this led to more feeding bouts. Our results demonstrate that monoterpenes influence the pattern of feeding of spruce budworm larvae as well as playing an important role in white spruce resistance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.946

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)0.0000.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations30
Published2010
Admission routes2
Has abstractyes

Explore more

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