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Record W1913909191 · doi:10.1111/afe.12058

Mass trapping wild <i>Agriotes obscurus</i> and <i>Agriotes lineatus</i> males with pheromone traps in a permanent grassland population reservoir

2014· article· en· W1913909191 on OpenAlexaff
Robert S. Vernon, Roderick P. Blackshaw, Willem G. van Herk, Markus Clodius

Bibliographic record

VenueAgricultural and Forest Entomology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyPheromone trapTrappingPheromoneGrasslandPopulationEcologySex pheromoneZoologyTrap (plumbing)Botany

Abstract

fetched live from OpenAlex

Abstract A study was conducted to determine whether arrays of pheromone traps could be used to reduce populations of male Agriotes obscurus ( AO ) and Agriotes lineatus ( AL ) adults in a confined nonfarmed habitat (grassy dyke). Traps placed 3 m apart in 15 × 2 arrays captured significantly more AL than AO , although the trap catch varied with location for both species and was inversely related to the number of nearby competing traps. Models of beetle movement indicated that a considerable proportion of males ( AL : 18.4–71.8%; AO : 35.0–58.3%) collected in the arrays had moved in from elsewhere and that AL beetles are more active than AO beetles . AL beetles frequently entered AO traps unless both trap types were present in the array, whereas AO rarely entered AL traps. Concurrent catches in pitfall traps placed inside and outside pheromone trapping zones indicated the trap arrays significantly reduced male (but not female) AO and AL beetles inside their respective arrays, that AO traps reduced AL beetles in AO arrays, and that both AL and AO traps could potentially reduce the number of mating pairs in these arrays. The implications of these results in determining the efficacy of this approach as a click beetle 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 score0.103
Threshold uncertainty score0.991

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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