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Record W2895168918 · doi:10.1093/ee/nvy092

The Effect of Feeding and Mate Presence on the Pheromone Production of the Spruce Beetle (Coleoptera: Curculionidae)

2018· article· en· W2895168918 on OpenAlexafffund
Rylee Isitt, Katherine P. Bleiker, Deepa S. Pureswaran, N. Kirk Hillier, Dezene P.W. Huber

Bibliographic record

VenueEnvironmental Entomology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsAcadia UniversityCanadian Forest ServiceUniversity of Northern British ColumbiaUniversity of New Brunswick
FundersCanadian Forest ServiceUniversity of Northern British ColumbiaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsCurculionidaePheromoneBiologyBotanySex pheromonePEST analysisHost (biology)HorticultureZoologyEcology

Abstract

fetched live from OpenAlex

Dendroctonus rufipennis (Kirby, Coleoptera: Curculionidae) uses pheromone blends containing aggregative components (frontalin, verbenene, 1-methyl-2-cyclohexen-1-ol [MCOL], and seudenol) and an anti-aggregative component (3-methyl-2-cyclohexen-1-one [MCH]) to coordinate attacks against host trees, but little is known about the influence of external stimuli on pheromone production. We conducted feeding experiments followed by pheromone extractions to determine if feeding duration and mate presence affected pheromone production in D. rufipennis. Unfed beetles of both sexes produced very little of any pheromone component. Females fed for 48 h produced significantly more MCH and MCOL compared to those which fed for 24 h. Males fed for 48 h produced significantly less seudenol than those which fed for 24 h. Male presence did not significantly affect female pheromone production. We propose that the pheromone blend produced by beetles transitions from aggregative to anti-aggregative shortly after colonizing a host, regardless of mate presence.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.837

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 studies0.0000.002
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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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