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Physical characterization of biobased corrugated panels, an innovative material

2023· article· en· W4385068867 on OpenAlexafffund
Adrien Gaudelas, Pierre Blanchet, Louis Gosselin, Matheus Roberto Cabral

Bibliographic record

VenueBioResources · 2023
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversité Laval
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCharacterization (materials science)Composite materialBuilding envelopeMoistureEnvelope (radar)Computer scienceNanotechnology

Abstract

fetched live from OpenAlex

Corrugated panels possess excellent thermomechanical properties, but, with presently no hygrothermal applications for building envelopes, their full potential still needs to be explored. This study characterized the physical properties of three biobased corrugated compositions to identify potential building applications. Flat samples of the same compositions were analyzed for certain properties to aid in understanding. As this characterization is groundbreaking, the testing was based on or inspired by established standards. Results suggest that the panels with two wood veneer cores and two kraft paper surfaces coated with polymers are the most promising, as such structures are less sensitive to water and possess a good moisture buffer value that should be advantageous for building construction. Corrugated panels are particularly interesting because their inner materials have properties comparable to those of conventional wood-based panels such as plywood. However, the apparent properties of corrugated panels become one to ten times smaller or larger, which opens up new design possibilities for building envelope applications.

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 score0.028
Threshold uncertainty score0.454

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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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