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Record W4200107041 · doi:10.3390/f12121725

Are Climates in Canada and the United States Suitable for the European Spruce Bark Beetle, Ips typographus, and Its Fungal Associate, Endoconidiophora polonica?

2021· article· en· W4200107041 on OpenAlexafffundabout
Kishan R. Sambaraju, Chantal Côté

Bibliographic record

VenueForests · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNatural Resources Canada
KeywordsBark beetlePicea abiesEcologyClimate changeBiologyHabitatBark (sound)Geography

Abstract

fetched live from OpenAlex

Invasions of exotic forest insects and pathogens can devastate evolutionarily naïve habitats and could cause irreversible changes to urban and natural ecosystems. Given the ever-increasing volume of trade in wood and plant stock worldwide, establishment of non-native pests under climate change is one of the most important forest health concerns currently. The European spruce bark beetle, Ips typographus, is a damaging, phloem-feeding insect of Norway spruce, Picea abies, in Eurasia. Endoconidiophora polonica is the most virulent ophiostomatoid fungal associate of I. typographus. Spruce species native to North America are susceptible to this insect-pathogen complex. We studied the suitability of ‘current’ (1970–2000) and future climates (2021–2100) in Canada and the United States for these two species via ensemble species distribution models. We also determined overlapping regions favorable to both I. typographus and E. polonica. Our results indicate that, currently, climate is particularly suitable for I. typographus and E. polonica in western Canada and throughout the United States. Northward shifts in climatic suitability are projected to occur in Canada for both species under climate change. By the end of the 21st century, a coast-to-coast corridor of climatic suitability for I. typographus and E. polonica will occur in Canada under high-temperature regimes.

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)0.001
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 score0.710
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.202
Teacher spread0.194 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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