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Record W2100613268 · doi:10.1603/0046-225x-31.5.766

Effect of Verbenone on Attraction of Predatory and Woodboring Beetles (Coleoptera) to Kairomones in Lodgepole Pine Forests

2002· article· en· W2100613268 on OpenAlexaff
B. Staffan Lindgren, Daniel R. Miller

Bibliographic record

VenueEnvironmental Entomology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsBiologyKairomoneAmbrosia beetleBark beetleLonghorn beetlePredationPheromoneAttractionBark (sound)BotanyEcologyCurculionidae

Abstract

fetched live from OpenAlex

The response of bark beetle predators and woodboring beetles to the bark beetle anti-aggregation pheromone, verbenone, was tested in the field with multiple-funnel traps baited with attractant kairomones. Catches of the predators Thanasimus undatulus (Say), Enoclerus sphegeus (F.), Enoclerus lecontei (Wolcott) (Coleoptera: Cleridae), and Lasconotus complex LeConte (Coleoptera: Colydiidae) declined significantly with increasing release rates of verbenone. Lasconotus subcostulatus Kraus, and Corticeus praetermissus (Fall) (Coleoptera: Tenebrionidae) (in two of three experiments), showed no significant response to verbenone. In a third experiment, catches of C. praetermissus increased with verbenone dose. Likewise, catches of the striped ambrosia beetle, Trypodendron lineatum (Olivier) (Coleoptera: Scolytidae), increased with verbenone dose in one experiment, but there was no effect in two other experiments. Verbenone had no effect on the response of Spondylis upiformis Mannerheim (Coleoptera: Cerambycidae). We hypothesize that predators specializing on early successional bark beetles are repelled by verbenone since this compound indicates a late stage attack. Generalist predators and woodborers either do not respond to verbenone at all, or they may be attracted by it.

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 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 score0.068
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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

Citations26
Published2002
Admission routes1
Has abstractyes

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