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Record W1966870550 · doi:10.1139/x01-170

Relationship between spruce beetle and tomentosus root disease: two natural disturbance agents of spruce

2002· article· en· W1966870550 on OpenAlexvenueno aff
Kathy J. Lewis, B. Staffan Lindgren

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyTaigaDisturbance (geology)BorealBotanyEcology

Abstract

fetched live from OpenAlex

This project investigated the interaction between tomentosus root disease of spruce, caused by Inonotus tomentosus (Fr.:Fr.) S. Teng, and spruce beetle (Dendroctonus rufipennis (Kirby)). Both organisms are important agents of mortality and volume loss in boreal and sub-boreal spruce forests of British Columbia. They also occur in similar stand types with respect to species composition and tree age. One study involved an intensive survey of 23 spruce stands, where trees were sampled for both beetle and root disease. Tree condition (dead standing, live, windthrown) was also recorded. Few stands showed a significant relationship between incidence of spruce beetle and incidence of root disease, regardless of tree condition. Observations indicated that beetles actually tended to avoid severely infected trees. A second study involved pheromone baiting of paired healthy and infected trees, and measurements of phloem thickness. Two sites were used, one with very high (epidemic) populations of beetles, and the second with low (endemic) levels. Spruce beetle attacks were more successful on infected trees compared with healthy trees only at the site with endemic levels of beetle. Collectively, the results indicate that tomentosus root disease helps to maintain endemic levels of spruce beetle, and disease incidence may be useful as a tool to identify areas that may have endemic populations of spruce beetle.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.053
GPT teacher head0.305
Teacher spread0.253 · 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

Citations27
Published2002
Admission routes1
Has abstractyes

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