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Record W2051511671 · doi:10.1139/x10-213

An exotic insect and pathogen disease complex reduces aboveground tree biomass in temperate forests of eastern North America

2011· article· en· W2051511671 on OpenAlexvenueno aff
Posy E. Busby, Charles D. Canham

Bibliographic record

VenueCanadian Journal of Forest Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBeechBiomass (ecology)BiologyEcologyTemperate forestTemperate climateTemperate rainforestHost (biology)Abundance (ecology)ForestryGeographyEcosystem

Abstract

fetched live from OpenAlex

Forests store a large portion of global carbon in tree and soil biomass. However, our understanding of the factors that may reduce rates of forest carbon accumulation is incomplete. This study examines the impact of an exotic insect and fungal pathogen disease on aboveground tree biomass in forests of eastern North America. We determine how beech bark disease (BBD) — a pervasive but nonextirpating disease — influences the growth and survival of its host tree, Fagus grandifolia Ehrh., and the effects of changes in the demography of this late-successional dominant tree species on total stand-level aboveground tree biomass. Our analyses use US Forest Service Forest Inventory and Analysis data from eastern states located along a gradient in the time since introduction of BBD. In Maine, where BBD has been present for >50 years, we observed reduced growth and survival of the host tree and reduced overall stand-level aboveground tree biomass compared with states where BBD arrived more recently. Additionally, there is a negative relationship between host tree abundance and overall stand-level aboveground tree biomass. Where beech is most abundant, BBD results in substantial declines in aboveground tree biomass (e.g., 11% in Maine); where beech is less abundant, we expect more modest declines (1%–4%).

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 score0.797
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.287
Teacher spread0.191 · 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
Published2011
Admission routes1
Has abstractyes

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