MétaCan
Menu
Back to cohort
Record W3133493579 · doi:10.23967/emus.2019.014

Comparision of Density Measurement Methods for Unidirectional Flax-Epoxy Polymer Composites

2020· article· en· W3133493579 on OpenAlexaff
Yousef Saadati, Jean-François Châtelain, Gilbert Lebrun, Yves Beauchamp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsMcGill UniversityÉcole de Technologie SupérieureUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsGas pycnometerComposite materialHeliumEpoxyMaterials sciencePolymerChemistryPorosity

Abstract

fetched live from OpenAlex

Density is a decisive factor in determining one of the most important advantages of flax fiber-reinforced polymer (FFRP) composites, i.e. their relatively low weight leading to high specific properties. As a fundamental physical property of composites, density enters in many engineering design and quality control calculations and its value is a determining factor for several applications. Especially, we need precise material properties including density so as to develop efficient numerical models to these materials. In this work, three density measurement methods were evaluated and compared: Helium-gas pycnometry and Archimedes with two different immersing liquids, water and ethanol. The results show that Helium-gas pycnometry and Archimedes with ethanol gave similar and repeatable results, whereas using water resulted in much lower values. The density values were all in the range of reported values. However, Helium-gas pycnometry or Archimedes using ethanol are recommended for more precision in measuring the density of FFRP composites.

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 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: none
Teacher disagreement score0.338
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.067
GPT teacher head0.332
Teacher spread0.265 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicNatural Fiber Reinforced CompositesFrench-language works237,207