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Record W1981454698 · doi:10.1139/x10-055

Investigating the influence of climate, site, location, and treatment factors on damage to young lodgepole pine in southern British Columbia

2010· article· en· W1981454698 on OpenAlexafffundvenueabout
Jean L. Heineman, Donald L. Sachs, W. Jean Mather, Suzanne W. Simard

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of British Columbia
FundersMinistry of Forests, Lands and Natural Resource Operations
KeywordsPinus contortaMountain pine beetleClimate changeEcosystemEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) has been extensively planted throughout interior British Columbia, and as a result may be particularly susceptible to climate-induced changes in the range and severity of common damaging agents. We quantified the presence of 14 damaging agents in sixty-six 15- to 30-year-old pine stands. Hard pine stem rusts, primarily western gall rust, were present on every site. We used logistic regression to predict individual agent presence from climatic, location, site, and treatment factors and calculated odds ratios to evaluate whether risk to lodgepole pine increases or decreases as these factors change. Risk of damage from serious agents (stem disease, root disease, and mountain pine beetle) increased with increasing latitude; however, in several of these models, risk also increased as temperature of the coldest month got warmer. We also found evidence that increasing risk of damage from agents that are currently less serious (sequoia pitch moth, pine needle cast, and pine terminal weevil) was associated with warming and (or) increasingly dry climatic conditions. Given the predominance of lodgepole pine in northerly ecosystems and the prediction that climate change effects will increase with latitude, our results suggest the need to consider potential increases in damage from diseases and insects during silviculture planning and timber supply prediction.

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)0.000
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 score0.408
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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

Citations28
Published2010
Admission routes4
Has abstractyes

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