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Record W2028085998 · doi:10.5539/jmsr.v1n3p42

Mechanical Properties of Unidirectional Oriented Strand Board with Flat Vertical Density Profile

2012· article· en· W2028085998 on OpenAlexaffvenue
Costel Barbuta, Pierre Blanchet, Alain Cloutier

Bibliographic record

VenueJournal of Materials Science Research · 2012
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité LavalFPInnovations
Fundersnot available
KeywordsOriented strand boardMaterials scienceComposite materialRelative humidityPerpendicularShear modulusComposite numberHumidityGeometryMathematicsThermodynamics

Abstract

fetched live from OpenAlex

This study investigated the effect of the relative humidity (RH) and density of unidirectional oriented strand board (OSB) on parallel, perpendicular and through-the-thickness elastic modulus (E1, E2 and E3) and interlaminar shear modulus (G13 and G23). The relationships between these parameters and density were determined on unidirectional OSB panels with a flat density profile. The target densities were: 550 kg/m3, 700 kg/m3 and 850 kg/m3. Two RH levels at a constant temperature of 20°C were used to determine relationships between OSB mechanical properties and moisture content (MC). The analysis of variance (ANOVA) indicated that only density had a significant effect on the mechanical properties considered. Even though the effect of RH was not significant, a trend could be observed. Oriented strand board E1, E2, E3, G13 and G23, increased with an increase in density and decreased with an increase in RH. Finally, a regression procedure was used to determine the linear or quadratic equation necessary to predict the mechanical properties of the OSB panels as a function of density. A positive correlation was observed between the unidirectional OSB properties considered and density. These data will support the modeling of the hygromechanical behaviour of the OSB used as component in wood composite constructions.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.303
Teacher spread0.233 · 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 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

Citations4
Published2012
Admission routes2
Has abstractyes

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