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Record W4236432863 · doi:10.24124/2016/1234

Local and geographic variation in the pheromone blend of the spruce beetle, Dendroctonus Rufipennis Kirby (Coleoptera: Curculionidae)

2016· dissertation· en· W4236432863 on OpenAlexafffundabout
Rylee Isitt

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Forest ServiceAcadia UniversityU.S. Forest ServiceUniversity of Northern British ColumbiaDepartment of Natural Resources, Government of Newfoundland and Labrador
KeywordsPheromoneCurculionidaeBiologyPheromone trapPicea engelmanniiSex pheromoneEcologyBotanyMontane ecology

Abstract

fetched live from OpenAlex

The use of aggregation and anti-aggregation pheromones by spruce beetles has enabled the development of synthetic lures and repellants for monitoring and management purposes. However, the successful application of these tools across the spruce beetle’s large range may be stymied by geographic variation in the beetle’s response to and production of pheromone blends. Furthermore, a relative lack of published data on spruce beetle pheromone dynamics and regional pheromone variation may impede further research and the development of improved lures. Here I provide quantitative measurements of pheromone blends from spruce beetles obtained from numerous sites across Canada. I provide new evidence of geographic variation between the pheromone blends of beetles from eastern and western Canada, as well as within British Columbia and Alberta. I also show that feeding appears to be a prerequisite for pheromone production by spruce beetles, and that females transition from producing an aggregation pheromone... .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.254
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes3
Has abstractyes

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