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Record W2889593004 · doi:10.1177/096739111702500606

Auto-hybridization of Polyethylene/Maple Composites: The Effect of Fiber Size and Concentration

2017· article· en· W2889593004 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymers and Polymer Composites · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComposite materialMaterials scienceUltimate tensile strengthLinear low-density polyethylenePolyethyleneCompression moldingFiberModulusMapleExtrusionYoung's modulusIzod impact strength testMold

Abstract

fetched live from OpenAlex

This work investigated the effect of maple fiber size and content on the auto-hybridization of linear low density polyethylene (LLDPE) composites. The samples were compounded by twin-screw extrusion and molded by compression molding. Different fiber loadings (5 to 20% wt.), fiber sizes (0-425 microns) and size ratios (30/70, 50/50, 70/30 of short, medium, and long fibers) were used to prepare the auto-hybrid composites with 3% of coupling agent (maleated polyethylene). Micrographs and impact strength results showed that the fracture in auto-hybrid composites is mostly dependent on the longer fibers. At 10% wt. the optimum ratio was 30/70 of shorter/longer fibers, which improved tensile strength (20%), tensile modulus (20%), and impact strength (13%) compared with composites with a single fiber size. But at 20% wt., tensile modulus increased by 30% and torsion modulus by 40% above the rule of hybrid mixtures (RoHM) at a 70/30 ratio of shorter/longer fibers.

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.

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 score0.013
Threshold uncertainty score0.701

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 studies0.0010.001
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 head0.006
GPT teacher head0.236
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