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Record W2999959819 · doi:10.1515/ntrev-2019-0045

Materials characterization of advanced fillers for composites engineering applications

2019· article· en· W2999959819 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

VenueNanotechnology Reviews · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcGill University
FundersMinisterstvo Průmyslu a Obchodu
KeywordsMaterials scienceComposite materialCompactionPorositySurface energyRheologyWollastoniteCalcium carbonateSphere packing

Abstract

fetched live from OpenAlex

Abstract Four different minerals were investigated; hollow spheres of calcium carbonate, platy mica, needle like wollastonite and glassy perlite and characterized via iGC for surface energy, Freeman powder rheology for flow characterization, cyclic uniaxial die compaction for modulus of elasticity and frequency dependent sound absorption properties. Particle surface energy and particle shape strongly affected the packing density of powder beds. In the case of higher porosity and thus lower bulk density, the powders acoustic absorption was higher in comparison with higher packing density materials. Surface energy profiles and surface energy distributions revealed clear convergence with powder rheology data, where the character of the powder flow at defined consolidation stresses was mirroring either the high cohesion powders properties connected with the high surface energy or powder free flowing characteristics, as reflected in low cohesion of the powder matrix.

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 score0.167
Threshold uncertainty score0.395

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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