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Record W3106852512 · doi:10.22230/jem.2020v20n1a601

Long-Term Effects of Lodgepole Pine Terminal Weevil and Other Pests on Tree Form and Stand Structure in a Young Lodgepole Pine Stand in Southern British Columbia

2020· article· en· W3106852512 on OpenAlexaboutno aff
Lorraine Maclauchlan, Julie E. Brooks

Bibliographic record

VenueJournal of Ecosystems and Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWeevilHectareBiologyPEST analysisPinus contortaInfestationMountain pine beetleRust (programming language)AgronomyHorticultureBotanyEcologyAgriculture

Abstract

fetched live from OpenAlex

This study describes the impacts of 25 damaging agents recorded on young lodgepole pine trees over a 30-year period in a study plot in southern British Columbia. During the study, density fluctuated due to infill and mortality. Of the 1,295 stems per hectare present at the outset of the study, 37% of lodgepole pine died and only 24% of the trees remained pest-free by the final assessment. Pest-free trees were predominantly small and suppressed infill, leaving just over 1,000 stems per hectare of crop trees. Lodgepole pine terminal weevil affected over 38% of pine, with up to six attacks per tree. Fifty percent of lodgepole pine in the study was infected or killed by one or more hard pine stem rusts, with comandra blister rust and western gall rust being the predominant diseases, affecting 32% and 19% of the pine, respectively. Until age 20, 70% of weevil attacks caused major defects. From age 20–40 years, 50% of attacks caused major defects, often forks or multiple tops (stagheads). Defects were more severe when trees were attacked early in stand development. There was a strong correlation between the number of weevil attacks per tree and tree form, and the number of pests recorded per tree and tree form. Two or more pests per tree caused tree form to shift from good to moderate or poor.

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 score0.252
Threshold uncertainty score0.759

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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