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Record W2177510995 · doi:10.1139/x11-016

Wood quality in artificially inclined 1-year-old trees of <i>Eucalyptus regnans</i> — differences in tension wood and opposite wood properties

2011· article· en· W2177510995 on OpenAlexvenueno aff
Shakti Chauhan, John Walker

Bibliographic record

VenueCanadian Journal of Forest Research · 2011
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersUniversity of Canterbury
KeywordsPithTension (geology)ShrinkageComposite materialEucalyptusSolid woodMaterials scienceStiffnessBotanyHorticultureCompression (physics)Pulp and paper industryBiologyEngineering

Abstract

fetched live from OpenAlex

This paper presents a new approach to assess wood quality in 1-year-old Eucalyptus regnans F. Muell. Twenty-two seedlings were grown tilted to induce tension wood and acoustic velocity, basic density, longitudinal shrinkage, and volumetric shrinkage of both opposite wood and tension wood were assessed subsequently. Longitudinal growth strains were also estimated in the leaning stems by sawing along the length through the pith and measuring the bending of the two halves. The derived longitudinal growth strain, which varied from 708 to 2319 µε, was uncorrelated with stem and wood characteristics. Wood characteristics differed significantly between upper-side wood (predominantly tension wood) and lower-side wood (opposite wood). Tension wood was characterized by a higher acoustic velocity (high stiffness), basic density, and volumetric shrinkage compared with opposite wood. Tension wood also exhibited significant collapse and dimensional distortion such as twisting. Longitudinal shrinkage exhibited a significant negative relationship with acoustic velocity in opposite wood and a positive relationship with the basic density in tension wood. This new approach has potential in early selection of breeding material with superior normal wood properties from 1-year-old material by isolating the influence of tension wood. This approach can also be useful in understanding the variability in propensity of tension wood production in breeding populations.

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 score0.524
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.140
GPT teacher head0.281
Teacher spread0.141 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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