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Record W4387570947 · doi:10.3390/fluids8100275

Pipe Flow of Suspensions of Cellulose Nanocrystals

2023· article· en· W4387570947 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFluids · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReynolds numberTurbulenceLaminar flowNon-Newtonian fluidMaterials scienceNewtonian fluidMechanicsPower lawFlow (mathematics)Suspension (topology)PhysicsMathematics

Abstract

fetched live from OpenAlex

The pipeline flow behavior of suspensions of cellulose nanocrystals (CNCs) was investigated over the CNC concentration range of 0.24 to 3.65 wt% in different diameter pipelines. The CNC suspensions were Newtonian below the CNC concentration of 1 wt%. At higher concentrations, the CNC suspensions were non-Newtonian power-law fluids. For Newtonian CNC suspensions, the experimental friction factor–Reynolds number data were obtained only in the turbulent regime, and the data followed the Blasius equation closely. For power-law CNC suspensions, the experimental data of friction factor–Reynolds number covered both laminar and turbulent regimes. The experimental data followed the friction factor–Reynolds number relationships for power-law fluids reasonably well.

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 score0.003
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.302
Teacher spread0.271 · 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