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Record W2752295854 · doi:10.4039/tce.2017.39

Bioclimatic approach to assessing the potential impact of climate change on two flea beetle (Coleoptera: Chrysomelidae) species in Canada

2017· article· en· W2752295854 on OpenAlexafffundabout
O. Olfert, R.M. Weiss, R. H. Elliott, Juliana J. Soroka

Bibliographic record

VenueThe Canadian Entomologist · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaCanola Council of Canada
KeywordsFlea beetleBiologyBrassicaceaeClimate changeEcologyPopulationCruciferSpecies distributionBotanyHabitat

Abstract

fetched live from OpenAlex

Abstract Both the striped flea beetle, Phyllotreta striolata (Fabricius), and crucifer flea beetle, Phyllotreta cruciferae (Goeze) (Coleoptera: Chrysomelidae), are invasive alien species to North America. In western Canada, they are the most significant insect pests of cruciferous (Brassicaceae) crops. Climate is the one of the most dominant factors regulating the geographic distribution and population density of most insect species. Recent bioclimatic simulation models of the two flea beetle species fostered a better understanding of how the two species responded to selected climate variables. They demonstrated that selected climate variables increased population densities and geographic range of the two species. General circulation model inputs were applied in this study to assess the impact of a changing climate on the response of P. cruciferae and P. striolata populations. Model output, using the climate change scenarios, predicted that both P. cruciferae and P. striolata populations will shift north in future climates and the degree of geographic overlap between these two species will be greater than for current climate. This suggests that the two species could potentially cause economic losses over an expanded area in the future.

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 score0.237
Threshold uncertainty score0.649

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 studies0.0010.000
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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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
Published2017
Admission routes3
Has abstractyes

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