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Record W2982967780 · doi:10.1093/ee/nvz116

Cold Tolerance of Mountain Pine Beetle (Coleoptera: Curculionidae) Pupae

2019· article· en· W2982967780 on OpenAlexafffundabout
Katherine P. Bleiker, Gregory D. Smith

Bibliographic record

VenueEnvironmental Entomology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNatural Resources CanadafRI ResearchAlberta Agriculture and ForestryMinistry of Environment - SaskatchewanMinistry of Natural Resources
KeywordsCurculionidaeBiologyPupaBotanyBark beetleMountain pine beetleWoody plantEcologyLarva

Abstract

fetched live from OpenAlex

Determining the cold tolerance of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae), is critical for assessing its long-term persistence and eruptive potential in its new habitat, as well as the risk of continued range expansion across Canada's boreal forest. We used supercooling points (SCPs) and mortality assessments with exposure to different temperatures to determine the cold tolerance of pupae. Mountain pine beetle pupae cold tolerance did not increase with chilling and there was little change in the lethal temperature regardless of treatment or sample time. SCPs were reflective of expected mortality due to freezing: the lethal temperature for 50% mortality was -19.3°C and the mean SCP was -18.7°C. However, significant mortality occurred over time at much warmer temperatures (0 and -9°C), indicating that this life stage suffers significant prefreeze mortality. On the basis of our results, it is unlikely that pupae would be able to successfully overwinter in most regions in Canada. This study is part of a larger project aimed at producing a comprehensive assessment of the cold tolerance of all life stages of the mountain pine beetle to feed population models, climatic suitability indices, and spread assessments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.994

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)0.0290.007

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 head0.004
GPT teacher head0.193
Teacher spread0.190 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2019
Admission routes3
Has abstractyes

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