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Record W2153952522 · doi:10.1093/forestry/cpq047

Resistance of Sitka spruce (Picea sitchensis (Bong.) Carr.) to white pine weevil (Pissodes strobi Peck): characterizing the bark defence mechanisms of resistant populations

2011· article· en· W2153952522 on OpenAlexaffabout
John King, R. I. Alfaro, Manuel Gómez-López, Lara van Akker

Bibliographic record

VenueForestry An International Journal of Forest Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Forest ServiceGovernment of British Columbia
FundersU.S. Forest ServiceMcKnight Foundation
KeywordsWeevilBiologyResistance (ecology)PopulationWhite (mutation)Bark (sound)BotanyPopulation densityEcologyDemographyGene

Abstract

fetched live from OpenAlex

It has long been known that strong expressions of resistance to the white pine weevil (Pissodes strobi Peck) exist in certain Sitka spruce (Picea sitchensis (Bong.) Carr.) populations, particularly among trees originating from the Fraser Valley and the Qualicum area of British Columbia (BC). In this paper, we characterize how resistance is manifested in these known resistant populations. Specifically, using cloned individuals, we investigated resistant traits associated with repellency, constitutive resin canals (CRC) and sclereid or stone cells. Results indicate significant population differences in the level of these traits between these two populations and susceptible populations. Fraser Valley populations had four times the sclereid density of susceptible populations. Although the Big Qualicum (East Vancouver Island) population had the same high resistance as the Haney (Fraser Valley) population, it was expressed primarily through increased CRC. Sclereid cell density had the strongest correlation to weevil attack followed by CRC. We discuss pathways by which two distinct resistant populations may have developed in this high weevil hazard region of south-west BC.

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)0.002
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 score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.062
GPT teacher head0.321
Teacher spread0.259 · 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

Citations33
Published2011
Admission routes2
Has abstractyes

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Same venueForestry An International Journal of Forest ResearchSame topicForest Insect Ecology and ManagementFrench-language works237,207