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Record W2086238635 · doi:10.1002/app.34612

Porosity formation by biaxial stretching in polyolefin films filled with calcium carbonate particles

2011· article· en· W2086238635 on OpenAlexaff
Abou el kacem Qaiss, H. Saidi, O. Fassi‐Fehri, Mosto Bousmina

Bibliographic record

VenueJournal of Applied Polymer Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPorosityMaterials scienceHigh-density polyethylenePolyolefinCrystallinityPorosimetryCalcium carbonateComposite materialPolypropylenePolyethyleneComposite numberPolymerPorous medium

Abstract

fetched live from OpenAlex

Abstract Porous polymer films were generated by biaxial stretching of polypropylene (PP) and high‐density polyethylene (HDPE) filled with various amounts of calcium carbonate particles. The porosity of the films was measured by mercury porosimetry, and the obtained results were related to the processing conditions and to the morphology development during the biaxial stretching. The results showed that increasing the calcium carbonate (CaCO3) concentration and the draw ratio resulted in porosity increase for PP‐based composite films and in a decreased porosity in HDPE‐based composite films. Such peculiar behavior was connected to interfacial specific interactions between the matrix and the dispersed particles as well as to the crystallinity of the films. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.216
Teacher spread0.196 · 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 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

Citations27
Published2011
Admission routes1
Has abstractyes

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