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Record W2736566994 · doi:10.1139/cjp-2017-0062

Outlining the impact of second-order slip and multiple convective condition on nanofluid flow: A new statistical layout

2017· article· en· W2736566994 on OpenAlexvenueno aff
Nilankush Acharya, Kalidas Das, Prabir Kumar Kundu

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidPhysicsNonlinear systemMechanicsFlow (mathematics)Boundary value problemOrdinary differential equationConvective flowSlip (aerodynamics)Convective Boundary LayerMatrix similarityThermalShooting methodPartial differential equationConvectionStatistical physicsBoundary layerDifferential equationThermodynamicsHeat transfer

Abstract

fetched live from OpenAlex

An analysis exploring the influence of second-order slip mechanism on nanofluid flow passing over a permeable stretching surface is investigated. Additionally we have captured the flow features including the presence of realistic thermal and solutal boundary conditions. Applying the similarity transformation procedure leads us to convert the partial differential flow related equations into nonlinear ordinary ones. After that we solved them numerically using the fourth-order Runge–Kutta method in conjunction with the shooting technique. Parametric study has been performed through tables and diagrams to highlight the consequence of velocity, temperature, and concentration profile. Moreover, a statistical attempt is made to illustrate the correlation of physical parameters within the flow system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.255
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations40
Published2017
Admission routes1
Has abstractyes

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