MétaCan
Menu
Back to cohort

Models and Equations for Atomic Transport Coefficients of Liquid Metals: Viscosity and Self-Diffusivity

2007· article· en· W1971701307 on OpenAlexaff
Takamichi Iida, Nagendra Tripathi, M. Isac, Rod I.L. Guthrie

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Structural Properties of Metals and Alloys
Canadian institutionsMcGill University
Fundersnot available
KeywordsThermal diffusivityMelting pointThermodynamicsSurface tensionViscosityMaterials scienceLiquid metalMetalAluminiumSelf-diffusionMagnesiumPhysicsMetallurgyComposite material

Abstract

fetched live from OpenAlex

The current article presented appropriate models using a new parameter recently introduced by the authors to accurately predict the atomic transport coefficients, i.e. viscosity and self-diffusivity, of liquid metallic elements at their melting points. The models for both the meltingpoint viscosity and self-diffusivity are expressed in terms of well-known physical quantities; atomic mass, atomic volume, melting point, melting-point surface tension, and the new parameter T ξ . Moreover, the authors derived expressions for the temperature dependence of the atomic transport coefficients of liquid metallic elements in terms of melting point temperature. These two models give very good agreement with experimental data for various metallic liquids. Using the models, self-diffusivities were predicted for liquid aluminum, calcium, and magnesium.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0030.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 designTheoretical or conceptual
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

Citations12
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicThermodynamic and Structural Properties of Metals and AlloysFrench-language works237,207