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Record W2466633265 · doi:10.7717/peerj.2109

Gene expression analysis of overwintering mountain pine beetle larvae suggests multiple systems involved in overwintering stress, cold hardiness, and preparation for spring development

2016· article· en· W2466633265 on OpenAlexafffund
Jeanne A. Robert, Tiffany R. Bonnett, Caitlin Pitt, Luke J. Spooner, Jordie D. Fraser, Macaire M. S. Yuen, Christopher I. Keeling, Jörg Bohlmann, Dezene P.W. Huber

Bibliographic record

VenuePeerJ · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaSimon Fraser UniversityUniversity of Northern British Columbia
FundersCanada Research ChairsGenome AlbertaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaGenome British ColumbiaGenome Canada
KeywordsOverwinteringDendroctonusBiologyMountain pine beetleHardiness (plants)InsectTranscriptomeCurculionidaeBark beetlePopulationLarvaGeneEcologyGene expressionBotanyGeneticsCultivar

Abstract

fetched live from OpenAlex

Cold-induced mortality has historically been a key aspect of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae), population control, but little is known about the molecular basis for cold tolerance in this insect. We used RNA-seq analysis to monitor gene expression patterns of mountain pine beetle larvae at four time points during their overwintering period-early-autumn, late-autumn, early-spring, and late-spring. Changing transcript profiles over the winter indicates a multipronged physiological response from larvae that is broadly characterized by gene transcripts involved in insect immune responses and detoxification during the autumn. In the spring, although transcripts associated with developmental process are present, there was no particular biological process dominating the transcriptome.

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 score0.228
Threshold uncertainty score0.557

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)0.0000.000

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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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

Citations35
Published2016
Admission routes2
Has abstractyes

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