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Functionally Graded Biocomposites

2012· article· en· W2013508025 on OpenAlexaff
Mohamad Reza Barzegari, Denis Rodrigue

Bibliographic record

VenueMaterials science forum · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialDelamination (geology)Composite numberFunctionally graded materialUltimate tensile strengthStiffnessMaterial properties

Abstract

fetched live from OpenAlex

Functionally graded materials (FGM) are characterized by a gradual change in the volume fractions of two or more components as a function of position along certain dimensions. FGM has been introduced as an alternative to laminated composites where a mismatch in properties across each layer interface is the origin of stress concentration and a source of delamination/failure. In addition, the use of natural wood fibres as reinforcement has the advantage of easy manufacturing, low cost, biodegradability, negligible health hazards and high specific properties. Using short fibres in a controlled manner to produce functionally graded composites can provide more balanced properties and lead to improved stiffness/strength properties across thickness. The aim of this paper is to evaluate the mechanical behavior of functionally graded natural fibre composites. To study the effect of composite property variation, the functionally graded composite is divided into a number of homogeneous layers in order to evaluate the mechanical behavior. In particular, the effect wood fibre content variation across thickness on the tensile properties of the composites is presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.247
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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
Published2012
Admission routes1
Has abstractyes

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