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Record W4407898443 · doi:10.1139/cjfr-2024-0143

Anatomy of eucalyptus wood managed under a coppicing system

2025· article· en· W4407898443 on OpenAlexvenueno aff
Daniel de Souza Ribeiro, Tatiana de Fátima Martins Pires, Nauan Ribeiro Marques Cirilo, Vaniele Bento dos Santos, Graziela Baptista Vidaurre, Jordão Cabral Moulin

Bibliographic record

VenueCanadian Journal of Forest Research · 2025
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoppicingEucalyptusForestrySnagBiologyAgroforestryWoody plantGeographyBotanyEcologyHabitat

Abstract

fetched live from OpenAlex

Coppicing is a forest management technique that induces the production of new shoots from stumps to start a novel forest cycle. This study aimed to compare anatomical characteristics between coppice and high stem. Wood samples were collected from clones of Eucalyptus grandis × Eucalyptus camaldulensis, E. grandis × Eucalyptus urophylla, and Eucalyptus saligna grown in one-stem coppice, two-stem coppice, and high stem. Anatomical parameters of fiber dimensions such as vessel diameter and frequency were analyzed. Anatomical elements were quantified by cell coloring and point counting (324 points) methods. The anatomical structure of coppiced trees was found to vary according to eucalyptus species. Fiber length and vessel frequency were similar between coppice and high stem. Coppiced wood showed higher fiber cell wall thickness and vessel diameter. Adaptation to coppicing resulted in a specific anatomical composition for each species, such as a higher quantity of parenchymal cells in E. saligna and a lower quantity of fibers in E. grandis × E. urophylla. The point sampling method provided inaccurate results, differing significantly from the cell coloring method. The findings underscore that the decision to manage forests under a coppicing system should be approached with caution, as it is crucial to understand the resulting anatomical alterations to mitigate potential industrial issues.

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)0.001
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 score0.540
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.310
Teacher spread0.278 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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