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Record W4318777640 · doi:10.3390/f14020269

The Role of Organ and Leaf Habit on the Secondary Xylem Anatomy Variation across 15 Species from Brazilian Cerrado

2023· article· en· W4318777640 on OpenAlexaff
Rafaella Dutra, Anselmo Nogueira, Sergio Rossi, Larissa Chacon Dória, Valentinà Buttò, Carmen Regina Marcati

Bibliographic record

VenueForests · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Chicoutimi
FundersConsejo Nacional de Ciencia y TecnologíaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsXylemEvergreenBiologyDeciduousParenchymaHabitBotanyWoody plant

Abstract

fetched live from OpenAlex

Xylem is a complex tissue connecting the organs of plants and it performs multiple functions, including water transport, mechanical support, and storage. Because of the interaction between structure and function, xylem anatomy can provide useful information about its role in plant strategies. However, knowledge of how xylem anatomical traits change across organs and species functional groups is still limited. Here, we tested the role of different plant organs (stem and roots) and leaf habits (deciduous, semi-deciduous, and evergreen) on xylem anatomy variation across 15 woody species from the Brazilian Cerrado. Vessels, fibers, and parenchyma traits were measured on 45 individuals sampled in 2014 in Botucatu, São Paulo, Brazil. Our results revealed a higher parenchyma fraction and less fiber fraction in roots than in stems across species. Differences in wood anatomical traits between organs were mainly species-specific in parenchyma traits rather than vessel and fiber traits. Across leaf habits, only the root ray fraction was higher in evergreen species compared to deciduous species. These findings highlight a potential role of organs and leaf habits in xylem storage across Cerrado woody species.

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 score0.200
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)0.0000.000

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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