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Effects of heat and steam on the mechanical properties and dimensional stability of thermo-hygromechanically-densified sugar maple wood

2017· article· en· W2765292699 on OpenAlexafffund
Qilan Fu, Alain Cloutier, Aziz Laghdir

Bibliographic record

VenueBioResources · 2017
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsService de Recherche et d'EXpertise en Transformation des Produits ForestiersUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialMapleCompression (physics)Flexural strengthSteam explosionSwellingBendingBending stiffnessSuperheated steamSugarStiffnessCompression setPulp and paper industryBoiler (water heating)ChemistryWaste managementBotany

Abstract

fetched live from OpenAlex

Effects of heat and steam were investigated relative to the mechanical properties and dimensional stability of thermo-hygromechanically-densified sugar maple wood (Acer saccharum Marsh.). The densification process was performed at four temperatures (180 °C, 190 °C, 200 °C, and 210 °C) with and without steam. The hardness, bending strength, bending stiffness, and compression set recovery of the control and densified samples were determined. The effects of heat and steam on the density profile of the samples across thickness were also investigated. The results suggested that the effects of steam on the mechanical properties and dimensional stability of sugar maple wood were more important than that of heat’s influence. Compared to the samples densified without steam, the samples densified with steam showed higher values for hardness, bending strength, bending stiffness, compression set, and density, but much lower compression set recovery when treatment temperature was below 200 °C. High temperature combined with steam contributed to decreased compression set recovery. The lowest compression set recovery was obtained after the first swelling/drying cycle for all of the treatments. A higher weight loss occurred at 210 °C, which resulted in a noticeable decrease of wood density.

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 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 score0.017
Threshold uncertainty score0.298

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 head0.027
GPT teacher head0.190
Teacher spread0.163 · 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

Citations23
Published2017
Admission routes2
Has abstractyes

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