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Record W2125000493 · doi:10.1139/x05-241

Mountain pine beetle population sampling: inferences from Lindgren pheromone traps and tree emergence cages

2006· article· en· W2125000493 on OpenAlexvenueno aff
Barbara Bentz

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCurculionidaePheromoneMountain pine beetleBiologyPheromone trapDendroctonusPinus contortaPopulationEcologyBark beetle

Abstract

fetched live from OpenAlex

Lindgren pheromone traps baited with a mountain pine beetle (Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae)) lure were deployed for three consecutive years in lodgepole pine stands in central Idaho. Mountain pine beetle emergence was also monitored each year using cages on infested trees. Distributions of beetles caught in pheromone traps and emergence cages were compared. Each year, mountain pine beetle emergence from infested trees occurred within a 30-d period, although beetles were caught in pheromone traps over a period as long as 130 d. A large proportion of the total number of beetles caught in pheromone traps occurred prior to and following peak emergence from infested trees. Beetles caught in pheromone traps during the main emergence period from infested trees had greater whole-body lipids compared to beetles caught early and late in the flight season. Low lipid content of beetles caught before and after the main emergence period could be the result of a long-distance flight caused by fewer sources of pheromone attraction on the landscape and (or) some proportion of reemerged parents in the sample. Results suggest that pheromone traps disproportionately sample mountain pine beetle populations and that natural pheromone sources may influence the number and timing of beetles caught in synthetically baited traps.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.300
Teacher spread0.257 · 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.

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

Citations71
Published2006
Admission routes1
Has abstractyes

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