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Record W2076435809 · doi:10.1139/p07-048

MHD-mixed convection and mass transfer from a vertical stretching sheet with diffusion of chemically reactive species and space- or temperature-dependent heat source

2007· article· en· W2076435809 on OpenAlexvenueno aff
Mohamed Abd El-Aziz, Ahmed M. Salem

Bibliographic record

VenueCanadian Journal of Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsSherwood numberNusselt numberPhysicsMechanicsCombined forced and natural convectionNatural convectionMagnetohydrodynamicsMass transferHeat transferThermodynamicsBoundary layerReynolds numberMagnetic fieldTurbulence

Abstract

fetched live from OpenAlex

The influence of chemical reactions on the problem of coupled heat and mass transfer by natural convection from a vertical stretching surface in the presence of a space- or temperature-dependent heat source effect is investigated. The sheet is stretched linearly in the presence of a uniform transverse magnetic field. The fluid is assumed to be viscous and incompressible. The governing differential equations are transformed by introducing similarity variables into a system of nonlinear ordinary differential equations and solving them numerically on the assumption of a small magnetic Reynolds number. The effects of the various parameters on the velocity, temperature, and concentration profiles as well as the local wall shear stresses, the local Nusselt number, and the local Sherwood number are presented graphically and in tabular form. An analysis of the results obtained shows that the flow field is influenced appreciably by the chemical reaction, heat source, magnetic field, and suction or injection at the sheet. PACS No.: 47.65.-d

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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

Citations39
Published2007
Admission routes1
Has abstractyes

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