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An effective strategy for trapping Douglas‐fir beetle, <i>Dendroctonus pseudotsugae</i> (Col., Scolytidae) using combinations of unbaited and pheromone baited funnel traps

2002· article· en· W2040764303 on OpenAlexaff
W. G. Laidlaw, H. Wieser

Bibliographic record

VenueJournal of Applied Entomology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiologyTrappingTrap (plumbing)FunnelPheromonePheromone trapHorticultureBotanyEcologyPhysicsEnvironmental science

Abstract

fetched live from OpenAlex

Experiments were conducted to confirm and quantify earlier observations that unbaited funnel traps in conjunction with pheromone baited funnel traps may substantially increase trap catches of Douglas‐fir beetles, Dendroctonus pseudotsugae . In 12 replicates of one of these experiments during 1998 and 1999, the catches of a single baited control trap were compared to those of a set of four traps where the central trap was baited and a ring of three traps, 1 m from the central trap arranged in a star configuration, were unbaited. In the second experiment conducted during 1999, a ring of three unbaited traps was placed at 2 m and a second at 5 m from the central baited trap. Statistically robust results demonstrated clearly that the configuration of one central baited trap plus three unbaited satellite traps collected twice as many beetles on average over the season compared to the baited control trap. Addition of a second ring of unbaited traps increased collections by only about 20%, but this experiment indicated that trap catches dropped off exponentially with distance from the centre. The number of beetles caught in the baited traps was essentially the same in all 3 arrangements, suggesting that the additional unbaited traps captured beetles that otherwise may not have been captured.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.747

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)0.0000.000

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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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 designBench or experimental
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

Citations3
Published2002
Admission routes1
Has abstractyes

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