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Record W1573628440

MECHANICAL PROPERTIES IN RELATION TO SELECTED WOOD CHARACTERISTICS OF BLACK SPRUCE

2006· article· en· W1573628440 on OpenAlexfundno aff
Jérôme Alteyrac, Alain Cloutier, Chhun-Huor Ung, S. Y. Zhang

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2006
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofibrilYoung's modulusElasticity (physics)Composite materialMaterials sciencePithElastic modulusModulusFlexural strengthSoftwoodGeometryMathematicsBotanyChemistry
DOInot available

Abstract

fetched live from OpenAlex

The relation between ring width, ring density, microfibril angle, and bending properties was analyzed at 2.4-m height on twelve 80-year-old black spruce trees. The moduli of elasticity and rupture were measured in the southernmost radial direction on extracted specimens of size 10 x 10 x 150 mm3 from pith to bark. Ring density and ring width were measured by X-ray densitometry, and microfibril angle was measured by the Silviscan technology. The impact of these three traits on the moduli of elasticity and rupture was evaluated by explicitly separating the radial variation from the variation among trees using a mixed model analysis. The results obtained show first that the modulus of elasticity is negatively correlated to microfibril angle. This result supports the assumption that the relation between modulus of elasticity and microfibril angle is not dependent on radial growth rate. Secondly, ring density has a lower contribution in predicting the modulus of elasticity than the modulus of rupture. In both cases, ring width was not a significant factor of variation of the moduli of elasticity and rupture.

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)0.000
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations65
Published2006
Admission routes1
Has abstractyes

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