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Effect of relative humidity level on bonding properties of black spruce glulam

2025· article· en· W4408304048 on OpenAlexaff
João Vítor Felippe Silva, Antoine Cogulet, Pierre Blanchet, Quentin Pechon

Bibliographic record

VenueBioResources · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialRelative humidityBlack spruceHumidityForestryMeteorologyPhysics

Abstract

fetched live from OpenAlex

Glued-laminated timber (glulam) is a structural wood-based composite widely used in construction. One of the constraints to its production is the requirement that lamellae must have the same moisture content before bonding. This study analyzed the effect of joining lamellae having different moisture content levels on the bonding performance of glulam elements. Black spruce [Picea mariana (Mill.) B.S.P.] wood with different levels of moisture content (11%, 13%, and 15%) were bonded with two component polyurethane and placed in three different environments with different relative humidities. Block shear and delamination tests were performed according to ASTM D905-08 (2021) and EN 302-2 (2013), respectively, and the glue line profile was assessed with a micro-CT scanner. The results showed that the relative humidity had more influence on the results than the initial moisture of the wood. The results obtained for block shear and delamination tests respected the limits of the standard, but the adhesive profile of mixed moisture glulam presented some undesirable characteristics (thinner and irregular adhesive distribution). Micro-CT scan reconstructed images were found to be an interesting tool for this type of evaluation.

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 score0.123
Threshold uncertainty score0.390

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 head0.025
GPT teacher head0.228
Teacher spread0.202 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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