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Record W3184136259 · doi:10.1111/afe.12461

Prediction of the potential global distribution of the Asian longhorned beetle <i>Anoplophora glabripennis</i> (Coleoptera: Cerambycidae) under climate change

2021· article· en· W3184136259 on OpenAlexaff
Yuting Zhou, Xuezhen Ge, Zou Ya, Siwei Guo, Tao Wang, Shixiang Zong

Bibliographic record

VenueAgricultural and Forest Entomology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Guelph
FundersFundamental Research Funds for the Central Universities
KeywordsLonghorn beetleClimate changeAbundance (ecology)Southern HemisphereEcologyPEST analysisDistribution (mathematics)BiologyAgroforestryGeographyBotany

Abstract

fetched live from OpenAlex

Abstract The Asian longhorned beetle (ALB) Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) is an important wood‐boring pest that has caused substantial damage to broadleaf trees in Asia, North America, and Europe. We used the modelling software CLIMEX to project the potential global distribution of ALB based on both historical (1987–2016) and future (2021–2050) climate conditions. ALB has possible hosts in 37 genera, and their known distributions were incorporated into the model to assess their effect on pest distribution. Suitable regions for ALB are predicted to be widely distributed under both historical and future climate conditions, and across all continents except Antarctica. With climate change, climate suitability would increase in the regions north of 30°N and decline in most regions south of 30°N. The area of most climate‐suitable regions would be covered by potential hosts, and optimum hosts would dominate. The possibility of ALB outbreaks in the Northern Hemisphere is much higher than in the Southern Hemisphere, owing to the richer abundance of hosts. These results provide theoretical guidance for developing effective ALB monitoring and mitigation measures.

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 score0.023
Threshold uncertainty score0.233

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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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

Citations27
Published2021
Admission routes1
Has abstractyes

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