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Post-thermal treatment of oriented strandboard (OSB) made from cypress (Cupressus Glauca Lam)

2009· article· en· W2121675946 on OpenAlexaboutno aff
Esmeralda Yoshico Arakaki Okino, Divino Eterno Teixeira, Cláudio Henrique Soares Del Menezzi

Bibliographic record

VenueMaderas Ciencia y tecnología · 2009
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsComposite materialMaterials scienceYoung's modulusCypressUrea-formaldehydeThermal stabilityChemistryBotanyAdhesive

Abstract

fetched live from OpenAlex

The objective of this research was to determine the physical and mechanical properties of oriented strandboard (OSB) using strands of Cupressus glauca Lam., before and after a thermal treatment, as well as to evaluate the susceptibility of the boards to fungi attack. Boards with nominal density of 0.70 g/cm3 were produced with 5% and 8% of urea-formaldehyde (UF) resin. Physical and mechanical properties were evaluated according to ASTM D 1037 (1991) standard and compared with CSA O437.0 and ANSI A.208.1 standards. All mechanical properties were higher than those values required by both standards, except the modulus of elasticity in parallel axis. The thermal treatment slightly reduced the modulus of elasticity and stress at proportional limit, both in perpendicular axis, however improved significantly dimensional stability. Dimensional stability of the treated OSB was improved at the lower resin level but did not reach the maximum value required by the Canadian standard. Biological assay showed that heat-treated cypress OSB exposed to P. sanguineus reduced mass loss from 39% to 50%, while for G. trabeum the reduction was from 40% to 49%. Post thermal treatment of manufactured OSB (190ºC, 720 s) can be the recommended method to reduce the hygroscopicity without great effect on mechanical properties and to protect panels against these fungi.

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 score0.139
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 head0.010
GPT teacher head0.200
Teacher spread0.191 · 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

Citations45
Published2009
Admission routes1
Has abstractyes

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