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Record W2738953459 · doi:10.1111/afe.12249

Determination of <i>Agriotes obscurus</i> ( <scp>C</scp> oleoptera: <scp>E</scp> lateridae) sex pheromone attraction range using target male behavioural responses

2017· article· en· W2738953459 on OpenAlexaff
Roderick P. Blackshaw, Willem G. van Herk, Robert S. Vernon

Bibliographic record

VenueAgricultural and Forest Entomology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPheromoneAttractionBiologyBiological dispersalSex pheromoneRange (aeronautics)Pheromone trapTrappingEcologyZoologyPopulationDemography

Abstract

fetched live from OpenAlex

Abstract A study was conducted to determine the attractive range of traps baited with Agriotes obscurus pheromone to male beetles in both still air and wind conditions. This information is crucial for evaluating the potential of mass trapping when aiming to reduce beetle populations. Groups of 10 beetles were released at 14 points spaced 1 m apart along a linear track, at one end of which was a pheromone and wind source. Beetle response to the pheromone and/or wind was recorded 150 s after release and characterized as orienting either towards or away from the pheromone and/or wind source. Data analysis indicated the attraction range of the sex pheromone is &lt;5 m in still air, which is considerably lower than estimates from previous studies and emphasizes the challenge of mass trapping this species in the field. The attraction range increased when there was air flow. Unexpectedly, not all male beetles respond to the pheromone, and beetles are inclined to move downwind even in the presence of pheromone. The latter finding suggests that wind direction may influence beetle dispersal and mate finding in the field. The implications of these results for determining the efficacy of mass trapping as a management approach are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.776

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.239
Teacher spread0.219 · 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
Published2017
Admission routes1
Has abstractyes

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