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Record W4225815685 · doi:10.21203/rs.3.rs-1497043/v1

Tracing the dispersal route of the invasive Japanese beetle Popillia japonica

2022· preprint· en· W4225815685 on OpenAlexaffabout
Agostino Strangi, Francesco Paoli, Francesco Nardi, Ken Shimizu, Troy Kimoto, Immacolata Iovinella, Giovanni Bosio, Pio Federico Roversi, Antonio Carapelli, Leonardo Marianelli

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Food Inspection Agency
FundersKrell InstituteHorizon 2020 Framework ProgrammeRegione PiemonteEuropean Commission
KeywordsPopilliaJapanese beetleBiological dispersalJaponicaTracingBiologyGeographyEcologyBotanyComputer scienceSociologyDemography

Abstract

fetched live from OpenAlex

Abstract The Japanese beetle, Popillia japonica, is a highly polyphagous Scarabeidae native to Japan that colonized North America and Azores in the last century and has recently invaded Italy and Switzerland. Considering its economic impact to the horticulture and turfgrass industries, this species was ranked within the EU priority pests list in 2019. In order to reconstruct the source of introductions of this pest, we investigated the genetic variability of P. japonica in its native and invaded areas worldwide by analyzing 9 microsatellite loci and two mitochondrial genes, COI and CytB. There are two populations of Japanese beetles, from a limited area within central Japan, that are likely the source of the North American population. Moreover, the microsatellite data and resulting phylogeographic reconstruction suggests that the two European populations originated from independent introductions from southeast (Azores) and northeastern (Italy and Switzerland) North America (1). (1) For simplicity, in this paper North America refers to Canada and USA

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes2
Has abstractyes

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