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Record W2907449655 · doi:10.21175/radj.2018.02.024

VISUALIZATION AND MORPHOLOGICAL CHARACTERIZATION OF INTEGRAL SKIN CELLULAR POLYMERIC COMPOSITES USING X-RAY MICROTOMOGRAPHY

2018· article· en· W2907449655 on OpenAlexafffund
Remon Pop‐Iliev, W. Y. Pao, Pedram Karimipour-Fard, Ghaus Rizvi

Bibliographic record

VenueRAD Association Journal · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray microtomographyCharacterization (materials science)VisualizationComposite materialMaterials scienceX-rayComputer scienceArtificial intelligenceNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

This paper focuses on 3-dimensional non-destructive characterization of the morphologies of integral-skin cellular polymeric composites using X-ray Microtomography. Rapid Rotational Foam Molding (RRFM) is a polymer processing technology that is capable of creating composites with intricate shapes that have a foamed core surrounded by an integral solid skin layer (similar to the structure of a bone). The analyzed specimens were extracted from composites processed in RRFM having a solid skin made of polypropylene (PP) grades combined with foamed cores made of both polyethylene (PE) and PP grades by implementing a suitable chemical blowing agent (CBA) in extrusion. The resulting cellular structures pertaining to the foamed core and the near-skin area were visualized and their morphological quality was evaluated in terms of cell size distribution and cell density. The stress-strain behavior and 3-dimensional structural changes were monitored and characterized with in-situ compression testing.

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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.405

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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