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Application of process-based modelling for interpretation of stable isotope variations in tree rings

2024· article· en· W4394871415 on OpenAlexaboutno aff
Nataliya Vasilieva, Svetlana Sofronova, В. В. Баринов, Anna V. Taynik, Tatyana V. Trushkina, Olga V. Churakova

Bibliographic record

VenueForestry Engineering Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsStable isotope ratioIsotopes of carbonHydrogenIsotopes of oxygenDendrochronologyCarbon fibersSubarctic climateTree (set theory)Isotopeδ13COxygenEnvironmental scienceCelluloseRing (chemistry)ChemistryTotal organic carbonEnvironmental chemistryGeologyMathematicsPaleontologyPhysicsAlgorithmNuclear physicsOceanographyOrganic chemistryNuclear chemistry

Abstract

fetched live from OpenAlex

Tree-ring width and stable isotopes (carbon, oxygen, and hydrogen) in tree-ring cellulose are widely used to reconstruct environmental conditions. The application of models capable to describe a variation of stable isotopes in annual tree rings can be a powerful tool for interpretation of environmental changes at the eco-physiological level. In this paper, we modelled carbon (delta13C), oxygen (delta18O), and hydrogen (delta2H) variations at the tree-ring level and compared results with measured data, obtained from the Mackenzie Delta River (68°30′ N, 133°48′ W) for the common period from 1901 to 2009. Using a process-based model (LPX), we were able to simulate carbon isotope values, which significantly (r = 0.29; p = 0.002) correlate with measured carbon isotope values. The model calculations for oxygen and hydrogen require further modifications and improvements, in particular the inclusion of the source of water used by the trees in the Canadian subarctic, as well as delta18O and delta2H in the water.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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