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Effects of Fire-retardant Treatment and Burl Wood Structure on Three-dimensional Changes of Sandwich Panels Made from Walnut Decorative Veneer

2017· article· en· W2738016617 on OpenAlexafffund
Jedi Rosero-Alvarado, Roger E. Hernández, Bernard Riedl

Bibliographic record

VenueBioResources · 2017
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVeneerFire retardantMaterials scienceComposite materialSoftwoodForensic engineeringEngineering

Abstract

fetched live from OpenAlex

The effects of a fire-retardant treatment and burl wood structure on the three-dimensional changes of aircraft sandwich panels were evaluated. Unvarnished and varnished panels with an outer decorative layer made from walnut burl (Juglans hindsii L.) were studied. Half of the samples from each type of panel received a fire-retardant treatment (phosphate-based) on all three layers of the decorative plywood. The other half had the two inner layers treated and the outer layer was left untreated. Three different wood areas formed by rotary peeling and by the grain orientation from the burl structure were identified and their veneer surfaces were separately studied. Samples pre-conditioned at 20 °C and 40% relative humidity (RH) underwent adsorption (25 °C, 90% RH) and then desorption (25 °C, 40% RH) treatments. Changes in the moisture content (MC), swelling, shrinkage, roughness, and waviness were measured after each moisture exposure condition. The results showed that the fire-retardant treatment significantly increased the MC, swelling, shrinkage, roughness, and waviness of the unvarnished and varnished panels. This treatment also affected the roughness and waviness of the burl wood structure for the unvarnished panels. The effect of this anatomical feature was not noticeable in the varnished panels.

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 score0.125
Threshold uncertainty score0.529

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 head0.013
GPT teacher head0.204
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.

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

Citations2
Published2017
Admission routes2
Has abstractyes

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