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Record W2051993043 · doi:10.1139/x07-227

Climate sensitivity of trembling aspen radial growth along a productivity gradient in northeastern British Columbia, Canada

2008· article· en· W2051993043 on OpenAlexafffundvenueabout
Giovanni Leonelli, Bernhard Denneler, Yves Bergeron

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersCanadian Forest Service
KeywordsProductivityPrecipitationEnvironmental scienceClimate changeClimate sensitivityDendrochronologyPhysical geographyDendroclimatologySubstrate (aquarium)ForestryGeographyClimatologyEcologyAtmospheric sciencesClimate modelBiologyMeteorologyGeology

Abstract

fetched live from OpenAlex

Growth–climate relationships were analyzed for 15 stands of trembling aspen ( Populus tremuloides Michx.) along a productivity gradient in northeastern British Columbia. Productivity was evaluated with a site index model whose values varied between 6.65 and 26.93 m height at age 50 years. To assess the role of climate on radial growth, we built a tree-ring chronology for each site and then performed a dendroclimatic analysis by means of bootstrapped correlation functions using monthly and annual climatic variables. We found that trembling aspen sensitivity to climate varied across stands with different productivities in the study area. At the more productive sites, trees were more sensitive to climate, and ring widths were positively correlated, particularly with summer month precipitation prior to the year of growth. The different responses to climate along the productivity gradient are likely related to the differences in substrate properties; substrates at the more productive sites are richer in nutrients and have a better water holding capacity than those of the less productive sites. Our study suggests that the effects of future climate change on tree growth may not only increase towards the limit of a species distribution, but also towards richer and wetter stands.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.247
Teacher spread0.209 · 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

Citations35
Published2008
Admission routes4
Has abstractyes

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