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Record W1033163757

A comparison of outbreak dynamics of the spruce bark beetle in Sweden and the mountain pine beetle in Canada (Curculionidae: Scolytinae)

2010· article· en· W1033163757 on OpenAlexaboutno aff
Simon Kärvemo, Leif Martin Schroeder

Bibliographic record

VenueEpsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences)) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMountain pine beetleDendroctonusBark beetlePinus contortaOutbreakCurculionidaeEcologyBark (sound)ForestryPicea abiesGeographyBiology
DOInot available

Abstract

fetched live from OpenAlex

The European spruce bark beetle (Ips typographus) and the North American mountain pine beetle (Dendroctonus ponderosae) may kill millions of trees during outbreak periods. Both species have also experienced large outbreaks in recent years. But the magnitude of the outbreaks of D. ponderosae is much larger. In this review we compare the outbreak history of I. typographus in Sweden with D. ponderosae in British Columbia in Canada. We also discuss some possible explanations for the difference in outbreak magnitude between the two species. During the last fifty years (1960-2009), three outbreaks of I. typographus have occurred in Sweden which resulted in a volume of about 9 million m3 of killed Norway spruces (Picea abies). During the same period D. ponderosae has killed about 600 million m3 of lodgepole pine (Pinus contorta) in British Columbia. Based on a literature review we suggest two factors that may contribute to the much more severe outbreaks caused by D. ponderosae: (1) a lower colonisation density needed by D. ponderosae to overcome tree defences and (2) a higher reproductive success of D. ponderosae in killed trees. In addition, the proportion of old stands, susceptible to bark beetle attacks, is much higher in British Columbia than in Sweden.

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 score0.162
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.222
Teacher spread0.214 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEpsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences))Same topicForest Insect Ecology and ManagementFrench-language works237,207