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Record W4401175827 · doi:10.3390/buildings14082359

Advancements and Applications of Wood-Based Sandwich Panels in Modern Construction

2024· article· en· W4401175827 on OpenAlexaff
Elena Vladimirova, Meng Gong

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSandwich-structured compositeEngineeringStructural engineeringConstruction engineeringMaterials scienceArchitectural engineeringForensic engineeringComposite materialComposite number

Abstract

fetched live from OpenAlex

Wood sandwich panels are widely utilized in residential, commercial, and industrial settings due to their excellent thermal insulation characteristics, ease of installation, and high strength-to-weight ratio. This review provides an overview on experimental outcomes demonstrating the structural integrity and versatility of wood sandwich panels. It highlights recent advancements in meeting payload requirements and their effectiveness in reducing costs and weights for prefabricated houses. The review focuses on structural applications and material efficiency, showcasing their roles in lightweight, durable constructions for retrofitting and new projects. The potential of novel, sustainable materials in construction is explored, addressing current challenges and emphasizing the diverse applications and environmental benefits of wood-based sandwich panels, underscoring their importance in advancing energy-efficient and sustainable construction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.224
Teacher spread0.218 · 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 designNot applicable
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

Citations25
Published2024
Admission routes1
Has abstractyes

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