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Record W4285310914 · doi:10.1007/978-3-030-92698-4_20

Stable Isotopes in Tree Rings of Boreal Forests

2022· book-chapter· en· W4285310914 on OpenAlexaffabout
Olga V. Churakova, Trevor J. Porter, Alexander V. Kirdyanov, Vladimir S. Myglan, Marina V. Fonti, Еugene А. Vaganov

Bibliographic record

VenueTree physiology/Tree physiology (Dordrecht) · 2022
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Toronto
FundersRussian Science Foundation
KeywordsSubarctic climateTaigaBorealTundraBoreal ecosystemPhysical geographyEnvironmental scienceClimate changeSteppeBlack sprucePermafrostEcologyGeographyArcticForestryGeologyOceanographyArchaeology

Abstract

fetched live from OpenAlex

Abstract The boreal forests are widely expanded from subarctic forest to tundra, and from taigato forest-steppe zone (from 50 °N to 70 °N). We reviewed available stable isotope chronologies in tree-ring cellulose (δ 13 C, δ 18 O and δ 2 H) from 16 sites located in the Russian Federation; 4 research sites from Fennoscandia (Finland, Sweden and Norway); 5 sites from Canada, and 1 site from Alaska (USA) to evaluate impact of climatic changes from seasonal to annual scale across boreal forest ecosystems. Results of our review of carbon isotope data showed that droughtconditions (mainly high vapour pressure deficit) are prevalent for western and central regions of Eurasia, Alaska and Canada, while northeastern and eastern sites of Eurasian subarctic are showing water shortage developments resulting from decreasing precipitation. Oxygen isotopechronologies show increasing trends towards the end of the twentieth century mainly for all chronologies, except for the Siberian northern and southern sites. The application of the multiple stable isotope proxies (δ 13 C, δ 18 O, δ 2 H) is beneficial to study responses of boreal forests to climate change in temperature-limited environments. However, a deeper knowledge of hydrogen isotope fractionation processes at the tree-ring cellulose level is needed for a sound interpretation and application of δ 2 H for climate reconstructions, especially for the boreal forest zone where forest ecosystems are more sensitive to climatic and environmental changes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

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