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Record W2228226530

Effects of pH on lap-shear strength for aspen veneer

2013· article· en· W2228226530 on OpenAlexafffund
Xiaodong Wang, Zeen Huang, Paul Cooper, Xiang‐Ming Wang, Yaolin Zhang, Romulo Casilla

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Toronto
FundersNatural Resources CanadaUniversity of Toronto
KeywordsVeneerHemicelluloseAdhesiveLigninBond strengthComposite materialShear strength (soil)ChemistryMaterials scienceDurabilityPulp and paper industryEnvironmental scienceLayer (electronics)Organic chemistry
DOInot available

Abstract

fetched live from OpenAlex

This study is one part of a whole project called "Impact of Extreme pH of Structural Adhesives on Bond Durability."The objective of this study was to evaluate effects of pH on woodadhesive bond strength and chemical change in aspen (Populus tremuloides Michx) wood caused by extreme pH exposures.Aspen veneer lap-shear samples were tested for maximum stress (N/mm 2 ) and wood failure (%) after exposure to soaking in different buffered solutions (pH ¼ 2.0, 2.5, 3.0; water, 10.0, 11.0, 11.5, 12.0, and 12.5) for 1, 4, and 7 mo.One set of samples stored in laboratory conditions was also tested as a control at each test time.Results indicated that bond strength and wood failure decreased after 4-and 7-mo exposures to acidic conditions but did not change significantly under alkaline exposures.However, the buffered acidic solutions (pH ¼ 2.0 and 3.0) did not cause a measurable chemical change in aspen wood, whereas losses in hemicellulose and lignin were found after aspen wood specimens had been exposed to pH 11.0 buffered solutions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 integrity0.0000.001
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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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 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

Citations3
Published2013
Admission routes2
Has abstractyes

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