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Effect of thermal modification on mechanical properties of Canadian white birch ( <i>Betula papyrifera)</i>

2011· article· en· W2075011108 on OpenAlexaffabout
S. Lekounougou, Duygu Kocaefe, Noura Oumarou, Yaşar Kocaefe, Sándor Poncsák

Bibliographic record

VenueInternational Wood Products Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceComposite materialYoung's modulusChemical modificationBetula platyphyllaMoistureDurabilityThermal stabilityThermalPulp and paper industryChemistryBotanyPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Wood is a renewable material widely used in the construction industry. However, it is susceptible to fungal degradation. Several chemical products have been developed to improve its durability, but the toxicity of some of these products limits their use. One alternative to chemical treatment is thermal modification of wood. This method improves the dimensional stability of wood and reduces its susceptibility to decay. The impact of different parameters (maximum temperature, heating rate, holding time and gas moisture content) of thermal modification on the mechanical properties of Betula papyrifera was studied in a prototype furnace. The results show a marked decrease in the modulus of rupture with increasing temperature while the modulus of elasticity does not seem to be affected. The hardness increases with maximum modification temperature, and in the absence of moisture in gas, and there is an improvement in the dimensional stability after thermal modification.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.036
GPT teacher head0.211
Teacher spread0.175 · 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

Citations26
Published2011
Admission routes2
Has abstractyes

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