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Thermo-Calc Program Interface and Their Applications - Direct Insertion of Thermodynamic and Kinetic Data into Modelling of Materials Processing, Structure and Property

2005· article· en· W2010111326 on OpenAlexaff
Qing Chen, Anders Engström, Lars Höglund, Henrik Strandlund, Bo Sundman

Bibliographic record

VenueMaterials science forum · 2005
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMaterials scienceInterface (matter)MATLABKinetic energyProperty (philosophy)Transformation (genetics)ToolboxComponent (thermodynamics)Phase (matter)ThermodynamicsComputer scienceChemistryProgramming languagePhysics

Abstract

fetched live from OpenAlex

Thermodynamic and kinetic data are generally essential for quantitative modeling of materials processing, structure, and property. Thermo-Calc program interfaces, including TQ, TCAPI, and TC MATLAB Toolbox, provides a hierarchy of APIs for application programmers to access thermodynamic and kinetic data via the kernel of Thermo-Calc and DICTRA, the most widely used software and database system for multi-component phase equilibrium and phase transformation calculations. With these program interfaces, variation of thermodynamic and kinetic properties can be directly obtained in real time as the local temperature, pressure, or composition changes. The structure and usage of the Thermo-Calc program interfaces will be introduced in this article. Successful application examples will be illustrated.

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)0.004
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: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.011

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 head0.017
GPT teacher head0.244
Teacher spread0.228 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

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