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Record W2107778590 · doi:10.1139/x07-228

Carbon cycling along a gradient of beech bark disease impact in the Catskill Mountains, New York

2008· article· en· W2107778590 on OpenAlexvenueno aff
Jessica E. Hancock, Mary A. Arthur, Kathleen C. Weathers, Gary M. Lovett

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersU.S. Forest ServiceUniversity of KentuckyU.S. Department of Agriculture
KeywordsBeechPlant litterMapleDominance (genetics)EcologyYellow birchCyclingHardwoodEnvironmental scienceEcosystemBotanyForestryBiologyGeography

Abstract

fetched live from OpenAlex

Exotic pests and pathogens, through direct and indirect effects on forest structure and species composition, have the potential to significantly alter forest ecosystem processes, including C cycling. Throughout the northern hardwood forest, beech bark disease (BBD) is causing widespread disruption in forest structure and composition. In the Catskill Mountains of New York, some forests formerly codominated by American beech ( Fagus grandifolia Ehrh.) and sugar maple ( Acer saccharum Marsh.) are shifting to sugar maple dominance. The effects of BBD and a subsequent shift in species composition on annual aboveground net primary production and soil CO 2 efflux were examined in eight forest plots selected to represent a gradient of BBD impact. There were no significant trends in aboveground net primary production across this gradient. However, growing season soil CO 2 efflux decreased linearly along the BBD gradient, declining by 40%. Although the mechanism controlling this decline is uncertain, the decrease in soil CO 2 efflux with BBD impact and a shift to greater composition of sugar maple in litterfall could significantly alter C cycling in northern hardwood stands in the Catskill Mountains.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.719

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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

Citations36
Published2008
Admission routes1
Has abstractyes

Explore more

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