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Record W2297916037 · doi:10.1002/ps.4247

Modelling the current and potential future distributions of the sunn pest <i>Eurygaster integriceps</i> (Hemiptera: Scutelleridae) using <scp>CLIMEX</scp>

2016· article· en· W2297916037 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePest Management Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsPEST analysisTemperate climateHemipteraEcologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The sunn pest, Eurygaster integriceps (Hemiptera: Scutelleridae), is an economically significant pest throughout Western Asia and Eastern Europe. This study was conducted to examine the possible risk posed by the influence of climate change on its spread. CLIMEX software was used to model its current global distribution. Future invasion potential was investigated using two global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), under A1B and A2 emission scenarios for 2030, 2070 and 2100. RESULTS: Dry to temperate climatic areas favour sunn pests. The potential global range for E. integriceps is expected to extend further polewards between latitudes 60° N and 70° N. Northern Europe and Canada will be at risk of sunn pest invasion as cold stress boundaries recede under the emission scenarios of these models. However, current highly suitable areas, such as South Africa and central Australia, will contract where precipitation is projected to decrease substantially with increased heat stress. CONCLUSION: Estimating the sunn pest's potential geographic distribution and detecting its climatic limits can provide useful information for management strategies and allow biosecurity authorities to plan ahead and reduce the expected harmful economic consequences by identifying the new areas for pest invasion. © 2016 Society of Chemical Industry.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.213
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