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Record W1593504535 · doi:10.15376/biores.7.3.3488-3499

Effects of thermal modification on mechanical and swelling properties and color change of lumber killed by mountain pine beetle

2012· article· en· W1593504535 on OpenAlexaff
Cai JiaBin, Liping Cai

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
FundersJiangsu Science and Technology DepartmentGovernment of Jiangsu Province
KeywordsSwellingComposite materialMaterials scienceYoung's modulusThermal treatmentThermalThermal stabilityMountain pine beetleModulusPulp and paper industryChemistryEcologyBiology

Abstract

fetched live from OpenAlex

To extend the application of mountain pine beetle (MPB) killed lumber for decking, siding, and landscaping materials, it is essential to improve its dimensional stability. Thermal treatment is one of the well-established processes used to improve wood stability by modifying chemical compounds and masking blue-stains by darkening the fibre color. In this study, the MPB lumber was subjected to thermal treatment at three temperatures (195, 205, or 215°C) and three exposure times (1.5, 2, or 3 h). Based on Duncan’s multiple range test, the results indicated that the volumetric swelling after thermal treatment, either from oven-dry to air-conditioned or from oven-dry to water-saturated, was significantly reduced after thermal treatment. Modulus of elasticity was increased when specimens were treated at a temperature of 195°C, and then decreased as the temperature increased. Modulus of rupture was significantly reduced as treatment temperature increased. The hardness of lumber thermal-treated at 195°C was significantly increased compared to that of the untreated lumber. At higher temperatures, hardness started to decrease slightly. With the treatment temperature increasing to 215°C for 3 h, the color difference between stained and clear wood was reduced by 75%. As a result, the blue-stains vanished gradually.

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 score0.011
Threshold uncertainty score0.253

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 head0.200
Teacher spread0.173 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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