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Record W1918168736 · doi:10.1111/een.12255

Evidence for mate‐encounter <scp>A</scp> llee effect in an invasive longhorn beetle ( <scp>C</scp> oleoptera: <scp>C</scp> erambycidae)

2015· article· en· W1918168736 on OpenAlexafffund
Marc Rhainds, Stephen B. Heard, Cory Hughes, Wayne E. MacKinnon, Kevin B. Porter, Jon Sweeney, Peter J. Silk, Ian DeMerchant, Sarah McLean, Garrett Brodersen

Bibliographic record

VenueEcological Entomology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of New BrunswickNatural Resources CanadaCanadian Forest Service
FundersCanadian Food Inspection Agency
KeywordsBiologyLonghorn beetleAbundance (ecology)MatingSex pheromoneSpermEcologyLimitingPheromoneZoologyBotany

Abstract

fetched live from OpenAlex

1. Limited empirical support is available for mate‐encounter A llee effects in invasive insects due to the logistical challenges of studying demographic trends in low‐density populations. 2. Traps baited with pheromone and spruce volatiles were used to monitor the abundance of female Tetropium fuscum F . ( C oleoptera: C erambycidae) at multiple sites in N ova S cotia in 2011 and 2012. Each female was dissected to determine the presence or absence of sperm in its spermatheca (mated or virgin female, respectively). 3. Both male and total T. fuscum abundance declined with increasing distance to the focal point of T. fuscum 's invasion. Female mating probability declined with male abundance and with distance from the invasion focus, and mating probabilities were very low at the most peripheral sites. Difficulty in encountering mates may thus contribute to limiting the spread of T. fuscum . 4. The approach outlined here could be integrated into existing surveys of wood borers using traps baited with semiochemicals to improve our understanding of the role of the mate‐encounter A llee effect in invasion dynamics.

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)0.003
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient 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 score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.037
GPT teacher head0.284
Teacher spread0.247 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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