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Record W1532595227

Effect of strand geometry and wood species on strandboard mechanical properties

2009· article· en· W1532595227 on OpenAlexfundno aff
Katherina Beck, Alain Cloutier, Alexander Salenikovich, Robert Beauregard

Bibliographic record

VenueCorpus Université Laval (Université Laval) · 2009
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsHardwoodMaterials scienceComposite materialCompressive strengthBendingBending stiffnessStiffnessFlexural strengthYoung's modulusBucklingBotany
DOInot available

Abstract

fetched live from OpenAlex

This study compared the performance of strandboards made from trembling aspen, a lowdensity hardwood species, with strandboards made from paper birch, a medium-density hardwood species.Strands were cut into three different lengths (78, 105, and 142 mm) and two thicknesses (0.55 and 0.75 mm) to compare the impact of species, strand geometry, specific surface, and slenderness ratio.Internal bond (IB), modulus of elasticity (MOE), and modulus of rupture (MOR) for flatwise and edgewise bending, compressive strength, and stiffness were all determined.Both species performed equally well in IB (0.73 MPa for both species combined).The highest MOE and MOR values in flatwise and edgewise bending were obtained for long, thin strands and were significantly lower for birch than for aspen panels (flatwise: 13.6 GPa and 99.2 MPa for aspen and 12.1 GPa and 85.5 MPa for birch; edgewise: 13.5 GPa and 66.3 MPa for aspen and 13.2 GPa and 65.7 MPa for birch).Short aspen strands resulted in the highest compressive properties, slightly higher than those of short birch strands (aspen: compressive strength 10.4 MPa and stiffness 1.22 GPa; birch: 10.8 MPa and 2.25 GPa, respectively).Strand length must therefore be a compromise between the need for high bending properties provided by long strands and the need for high compressive properties provided by short strands.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
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 head0.007
GPT teacher head0.161
Teacher spread0.154 · 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.

Study designBench or experimental
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

Citations26
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCorpus Université Laval (Université Laval)Same topicWood Treatment and PropertiesFrench-language works237,207