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Record W2011984415 · doi:10.1007/s11284-010-0705-3

Inter‐annual and inter‐plot variations of wood biomass production as related to biotic and abiotic characteristics at a deciduous forest in complex terrain, Korea

2010· article· en· W2011984415 on OpenAlexaff
Youngil Kim, Sinkyu Kang, Jong‐Hwan Lim, Dowon Lee, Joon Kim

Bibliographic record

VenueEcological Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsMcGill University
FundersKorea National ArboretumChungbuk National University
KeywordsDeciduousAbiotic componentEnvironmental scienceBiomass (ecology)Basal areaSpatial variabilityProductivityEcologyPrecipitationForest ecologyBiotic componentClimate changeEcosystemGeographyBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract This study aims to evaluate inter‐annual and inter‐plot variation of wood biomass production (WBP) and to investigate the relationships of the WBP variations with several biotic and abiotic characteristics at a deciduous forest in complex terrain, the Gwangneung Experimental Forest, Korea. Based on field survey in the plot‐scale study area, WBP during 1991–2004 was estimated by a dendrochronological method. Our field data indicated that the inter‐annual variation of WBP was closely related to the seasonal climate of both winter air temperature and spring precipitation. The inter‐plot variation of WBP was highly associated with basal area, biomass, and frequency of Quercus spp. in the plots, and correlations of the inter‐plot variation with the stand characteristics of the specific species were stronger than those with slope and soil water content. Our results suggest that the annual fluctuation of forest productivity is primarily governed by severe climate in a season of the year, and the spatial distribution of a dominant species largely represent plot variation in the productivity. Our findings contribute to an enhanced understanding of climatic effects on the annual variability of forest productivity and the spatial heterogeneity of the productivity, which are extensively concerned with forested ecosystems of Korea.

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)0.002
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 score0.066
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.336
Teacher spread0.299 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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