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Record W2587984231 · doi:10.5539/apr.v9n2p1

Model of Melting and Heat Transfer in Metals

2017· article· en· W2587984231 on OpenAlexvenueno aff
E. S. Filippov

Bibliographic record

VenueApplied Physics Research · 2017
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Structural Properties of Metals and Alloys
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceMelting pointThermodynamicsThermal expansionLambdaThermalPhase (matter)Electron transferHeat transferElectronCondensed matter physicsCrystallographyPhysicsPhysical chemistryChemistryMetallurgyComposite material

Abstract

fetched live from OpenAlex

Volumetric relationships under the thermal expansion of metals are analyzed. It is shown that the metals with the bcc structure possess a two-step structural change at the melting point: first, the transformation of the bcc structure to a fcc one takes place and then, liquid phase clusters with K = 12 are formed. The hexagonally packed (6 + 6) layered Cd and Zn change their structure from K=6 to K = 8 before melting. For the polymorphic transformations fcc (hcp) -- bcc, the value of thermal expansion was sufficient to change K = 12 for K = 8 long before the melting point. It is assumed that at high temperatures, thermal energy transfer is associated with the exchange fluctuations: higher electron density +K lambda and low electron density –K lambda over the coordinate of interatomic distances, wher lambda = h/mc and K is the number of nearest neighbors.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.100
GPT teacher head0.310
Teacher spread0.210 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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