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Record W1772339586 · doi:10.1111/afe.12045

Mark–recapture of <i>Agriotes obscurus</i> and <i>Agriotes lineatus</i> with dense arrays of pheromone traps in an undisturbed grassland population reservoir

2014· article· en· W1772339586 on OpenAlexaff
Robert S. Vernon, Willem G. van Herk, Roderick P. Blackshaw, Yoko Shimizu, 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
FundersBASF Corporation
KeywordsBiologyPheromone trapTrappingMark and recapturePheromonePopulationGrasslandEcologyZoology

Abstract

fetched live from OpenAlex

Abstract The present study was conducted using mark–released populations of male Agriotes obscurus ( AO ) and Agriotes lineatus ( AL ) adults to simulate the spatial and temporal capture rates of wild beetle populations in dense arrays of pheromone traps in a confined, nonfarmed habitat. Two parallel rows of traps, spaced 3 m apart along corridors of grassy dyke, recaptured 85.6% of AO and 77.8% of AL with arrays of their respective pheromone traps, mostly within the first week of release. In arrays of mixed AO and AL traps, recapture rates were 77.8% and 83.3%, respectively. In arrays with only AO traps, 31.2% of AL males released within the arrays mistakenly entered the AO traps, which declined to only 2.2% when released in arrays with both AO and AL traps. In arrays with only AL traps, only 0.7% of released AO were mistakenly taken in the AL traps, and only 0.3% mistakenly entered AL traps in mixed AO and AL trap arrays. Between 34.4–38.9% of AO and 21.1–25.6% of AL released in areas immediately adjacent to the trapping arrays were caught, mostly in the outermost traps. The implications of these results for determining the efficacy of mass trapping 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 score0.169
Threshold uncertainty score0.962

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 head0.222
Teacher spread0.211 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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