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Record W2805067803 · doi:10.1002/ajb2.1089

Differences in xylogenesis between dominant and suppressed trees

2018· article· en· W2805067803 on OpenAlexaff
Shushan Liu, Xiaoxia Li, Sergio Rossi, Li Wang, Wei Li, Eryuan Liang, Steven W. Leavitt

Bibliographic record

VenueAmerican Journal of Botany · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Chicoutimi
FundersChina Scholarship CouncilChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsXylemBiologyGrowing seasonTracheidCanopyBotanyCompetition (biology)Woody plantEcology

Abstract

fetched live from OpenAlex

PREMISE OF THE STUDY: Most dendroecological studies focus on dominant trees, but little is known about the growing season of trees belonging to different size classes and their sensitivity to biotic factors. The objective of this study was to compare the dynamics of xylem formation between dominant and suppressed trees of Abies fabri of similar age growing in the Gongga Mountains, southeastern Tibetan Plateau, and to identify the association between xylem growth and climate. METHODS: The timing and duration of xylogenesis in histological sections were investigated weekly during the 2013-2015 growing seasons. KEY RESULTS: Our investigation found that timing and duration of xylogenesis varied with canopy position and its associated tree size. Xylogenesis started 6-14 days earlier, and ended 5-11 days later in dominant trees than in suppressed trees, resulting in a significantly longer growing season. Dominant trees also exhibited higher temperature sensitivity of tracheid production rate than suppressed trees. CONCLUSIONS: The observed differences in xylogenesis among trees suggested that competition affects tree growth by reducing the growing period in suppressed trees. Representative climate-growth relationships should involve trees of all size classes when evaluating the effects of the environment on forest dynamics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.292

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 studies0.0000.001
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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