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Record W2793323218 · doi:10.3390/f9020086

Relationships between Wood Formation and Cambium Phenology on the Tibetan Plateau during 1960–2014

2018· article· en· W2793323218 on OpenAlexaff
Minhui He, Bao Yang, Vladimir V. Shishov, Sergio Rossi, Achim Bräuning, Fredrik Charpentier Ljungqvist, Jussi Grießinger

Bibliographic record

VenueForests · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Chicoutimi
FundersStudium Loire Valley-Institute for Advanced StudiesRussian Science FoundationNational Natural Science Foundation of ChinaAlexander von Humboldt-Stiftung
KeywordsCambiumPhenologyGrowing seasonXylemPlateau (mathematics)DendrochronologyEcosystemForest ecologyClimate changeVascular cambiumForest managementBotanyBiologyEcologyEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

The variability of tree stem phenology plays a critical role in determining the productivity of forest ecosystems. Therefore, we aim to identify the relationships between the timings of cambium phenology, and forest growth in terms of tree-ring width over a long-term scale. A meta-analysis was performed that combined the timings of xylem formation, which were calculated by a tree-ring formation model of the VS (Vaganov-Shashkin)-oscilloscope during the period 1960–2014, and a tree-ring width series at 20 composite sites on the Tibetan Plateau. Both the start and length of the growing season significantly affected the formation of wood at 70% of the 20 composite sites within the study region. A wider tree ring probably resulted from an earlier start and a longer duration of the growing season. The influence of ending dates on tree-ring width was less evident, and more site-dependent. Weak relationships were identified between the start and end of the growing season at 85% of the composite sites. Compared to the monitoring results, which could only detect the relationships between cambium phenology and xylem cell production from a limited number of trees and years, our long-term relationships deepened such connections, and therefore should be used to improve mechanism models for the accurate evaluating and predicting of wood production and carbon sequestration in forest ecosystems under current and future climate change.

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 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 score0.016
Threshold uncertainty score1.000

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)0.0000.001

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 head0.034
GPT teacher head0.234
Teacher spread0.200 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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