MétaCan
Menu
Back to cohort
Record W2461909756 · doi:10.1177/096739110601400303

Predicting the Elastic Modulus of Hybrid Fibre Reinforced Thermoplastics

2006· article· en· W2461909756 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 · 2006
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComposite materialMaterials scienceStiffnessModulusUltimate tensile strengthSynthetic fiberHigh-density polyethyleneYoung's modulusGlass fiberPolyethyleneFiber

Abstract

fetched live from OpenAlex

Hybrid fibre reinforced thermoplastics (containing more than one type of fibre) offer several design possibilities that do not exist with single fibre reinforced systems. Although there are extensive experimental data available in the literature on a variety of hybrid systems, a reliable and simple analytical model to predict the stiffness of these composites is not available. In this study, a modification of the hybrid rule of mixtures equation is developed to determine the stiffness properties of hybrid composites. Experimental data from single fibre reinforced thermoplastics forms the basis of the modification. Combinations of E-glass, hemp and hardwood flour were blended into high-density polyethylene in total fibre loadings of 10 to 60wt% to produce hybrid composites. The density and Young's modulus of the hybrid composites fell between the extreme values obtained for the single fibre composites. The modified rule of hybrid mixtures equation was found to adequately predict the tensile modulus of the hybrid 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.

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 score0.012
Threshold uncertainty score0.743

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 studies0.0000.000
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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