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Record W4393342104 · doi:10.1093/jisesa/ieae037

Impacts of winter warming events on spruce budworm: the importance of timing

2024· article· en· W4393342104 on OpenAlexafffund
Eric R.D. Moise, Jamie Warren, Joseph J. Bowden

Bibliographic record

VenueJournal of Insect Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceU.S. Forest ServiceNatural Resources CanadaDepartment of Fisheries and Land Resources
KeywordsSpruce budwormBiologyTortricidaeChoristoneura fumiferanaDiapauseDormancyLepidoptera genitaliaGlobal warmingEcologyOverwinteringPhenologyClimate changeAgronomyLarva

Abstract

fetched live from OpenAlex

Winter climate change constitutes not only a shift in chronic conditions (i.e., shorter length and warmer average temperatures) but will also influence the dynamics of extreme warming events. The latter may be particularly important for the performance and survival of insects, given their susceptibility to temperature variation. However, metabolic sensitivity changes over the course of winter diapause, and thus, insect responses to warming may vary depending on when the event occurs. To determine the influence of warm-up timing, we exposed the spruce budworm, Choristoneura fumiferana ((Clem.), Lepidoptera: Tortricidae), to acute warming events in early-, mid-, and late-dormancy and measured impacts on survival, performance, and biochemistry. While we did not observe any impacts of warm-up timing on performance, survival to the adult stage was significantly reduced in response to earlier warming. Additionally, glycogen concentration was significantly higher in response to early and late warming exposure. Collectively, these results suggest that the timing of extreme winter warming events matters, with consequences for both lethal and sublethal responses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.034
GPT teacher head0.282
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes2
Has abstractyes

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