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Record W2759463563 · doi:10.1002/2016jg003745

Radial Growth and Physiological Response of Coniferous Trees to Arctic Amplification

2017· article· en· W2759463563 on OpenAlexaboutno aff
Shunsuke Tei, Atsuko Sugimoto, Maochang Liang, Hitoshi Yonenobu, Yojiro Matsuura, Akira Osawa, Hisashi Sato, Junichi Fujinuma, Trofim C. Maximov

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationDendrochronologyTaigaEnvironmental scienceBorealPrimary productionDendroclimatologyClimate changeArcticPhysical geographyVegetation (pathology)ClimatologyAtmospheric sciencesEcosystemEcologyGeographyForestryGeologyBiologyMeteorology

Abstract

fetched live from OpenAlex

Abstract We describe the physiological responses of boreal conifers to climate change for the past 112 years using ring‐width and carbon isotope ratio (δ 13 C) chronologies at six forest sites in northern Eurasia and Canada. Responses differed among regions, depending on their climatic and/or geographic characteristics. Tree radial growth decreased over the past 52 years in central eastern Siberia with the higher rate of summer temperature increase than other regions, as indicated by the negative correlation between radial growth and summer temperature, but increased in northern Europe and Canada. Changes in tree‐ring δ 13 C indicated that recent climatic conditions have induced stronger drought stress for trees from central eastern Siberia than for those from other regions. The observed tree growth trends were compared to those simulated using a dynamic global vegetation model. Although the modeled annual net primary production (NPP) for trees generally exhibited similar decadal variation to radial growth, simulations did not show a recent decrease in tree growth, even in central eastern Siberia. This was probably due to an overestimation of the sensitivity of modeled tree NPP to precipitation. Our results suggest that the tree NPP forecasted under the expected future increases in temperature and average precipitation might be overestimated, especially in severely dry regions such as central eastern Siberia.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.102
GPT teacher head0.358
Teacher spread0.256 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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