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Record W2890229885 · doi:10.1111/nph.15477

Functions of stone cells and oleoresin terpenes in the conifer defense syndrome

2018· article· en· W2890229885 on OpenAlexafffund
Justin G. A. Whitehill, Macaire M. S. Yuen, Hannah Henderson, Lina Madilao, Kristina Kshatriya, Jennifer Bryan, Barry Jaquish, Jörg Bohlmann

Bibliographic record

VenueNew Phytologist · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsMinistry of ForestsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaGenome Canada
KeywordsOleoresinWeevilTerpeneBiologyBotanyLarvaShootHerbivorePEST analysisHorticultureCurculionidae

Abstract

fetched live from OpenAlex

Conifers depend on complex defense systems against herbivores. Stone cells (SC) and oleoresin are physical and chemical defenses of Sitka spruce that have been separately studied in previous work. Weevil oviposit at the tip of the previous year's apical shoot (PYAS). We investigated interactions between weevil larvae and trees in controlled oviposition experiments with resistant (R) and susceptible (S) Sitka spruce. R trees have an abundance of SC in the PYAS cortex. SC are mostly absent in S trees. R trees and S trees also differ in the composition of oleoresin terpenes. Transcriptomes of R and S trees revealed differences in long-term weevil-induced responses. Performance of larvae was significantly reduced on R trees compared with S trees under experimental conditions that mimicked natural oviposition behavior at apical shoot tips and may be attributed to the effects of SC. In oviposition experiments designed for larvae to feed below the area of highest SC abundance, larvae showed an unusual feeding behavior and oleoresin appeared to function as the major defense. The results support a role for both SC and oleoresin terpenes and possible synergies between these traits in the defense syndrome of weevil-resistant Sitka spruce.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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.

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

Citations40
Published2018
Admission routes2
Has abstractyes

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