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Record W3015239018 · doi:10.1515/npprj-2019-0084

Material characterisation for natural fibres: compressibility, permeability and friction

2020· article· en· W3015239018 on OpenAlexafffund
Keller J. H. George, Aly Ahmed, Andrew N. Hrymak, M. Hesham El Naggar

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOedometer testCompressibilityPermeability (electromagnetism)Materials scienceComposite materialDirect shear testGeotechnical engineeringRheologyShear (geology)GeologySoil waterEngineeringSoil scienceChemistry

Abstract

fetched live from OpenAlex

Abstract Although fibrous suspensions are finding increasing use in many applications, holistic studies investigating their material properties are essentially non-existent due to a lack of simple and reliable measurement techniques. To address this, geotechnical techniques have been considered for the characterisation of an extruded suspension of poplar fibres. Use of such characterization techniques was thought suitable since the extruded fibres share many physical similarities with fibrous peats, and since both materials are primarily derived from plants. In the present study, oedometer and direct shear tests were employed to evaluate the compressibility, permeability and friction properties of poplar fibres. Triplicates of each test were carried out for two differently prepared poplar fibres at several different initial water contents. All measurements were found to be in good agreement with the values typical of fibrous peats, justifying direct cross-over of geotechnical techniques as an alternative method for characterising and modelling fibrous suspensions in manufacturing applications.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.111
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.348
Teacher spread0.290 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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