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Record W4396702342 · doi:10.1016/j.isci.2024.109885

Phylogenetic conservatism and coordination in traits of Chinese woody endemic flora

2024· article· en· W4396702342 on OpenAlexaff
Jihong Huang, Qing Wang, Pablo Sanchez-Martinez, Yousry A. El‐Kassaby, Qiang Jia, Yifei Xie, Wenbin Guan, Runguo Zang

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersInstitute of Botany, Chinese Academy of SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsFlora (microbiology)Phylogenetic treeConservatismBiologyWoody plantPhylogeneticsEcologyEvolutionary biologyBotanyGeneticsPolitical science

Abstract

fetched live from OpenAlex

Range-limited endemic species, often labeled as endangered due to their low adaptability to climate change, exhibit unclear evolutionary mechanisms influencing their distribution. This study explores the relationship between leaf length, maximum height, and seed diameter and their linkage to phylogeny and climate in the macroecology of 1,370 woody endemics. Using Bayesian analytical method that allows partitioning phylogenetic and environmental variances and covariance, we revealed moderate to high phylogenetic signals in these traits, indicating evolutionary constraints potentially impacting climate change adaptability. The study uncovered a phylogenetically conserved coordination between height and leaf length which showed to be independent of macroecological patterns of temperature and precipitation. These findings emphasize the role of phylogenetic ancestry in shaping the distribution of woody endemics, highlighting the need for prioritized in-situ conservation and providing insights for ex situ conservation strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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