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Record W2523568150 · doi:10.1088/0957-0233/27/3/035905

A modified hot thermocouple apparatus for the study of molten oxide solidification and crystallization

2016· article· en· W2523568150 on OpenAlexaff
Shaghayegh Esfahani, Karim Danaei, Daniel Budurea, Mansoor Barati

Bibliographic record

VenueMeasurement Science and Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThermocoupleMaterials scienceTemperature measurementCalibrationPower (physics)CrystallizationOxideNuclear engineeringComposite materialThermodynamicsMetallurgyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The hot thermocouple technique (HTT) is an experimental method primarily used for studying high temperature phase transformations and interactions. The thermocouple driver is the main component of the apparatus, allowing heating and temperature measurement of the thermocouple that acts as a heating element and temperature sensor simultaneously. Previous setups have employed an ac (alternating current) power feed to the thermocouple, creating inherent limitations to the time interval and frequency of temperature sensing and control. In this article, the development of a dc (direct current)-based thermocouple driver is discussed. The new setup allows higher frequency (480 Hz) of heating pulses applied to the thermocouple. The higher switching frequency improves the thermocouple temperature reading accuracy and decreases the time interval between the measurements by a factor of eight, compared to existing HTT devices. The development of a dc power HTT apparatus, its calibration, and examples of its use in the studies of the crystallization of oxide mixtures are presented in this article.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.035
GPT teacher head0.236
Teacher spread0.200 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2016
Admission routes1
Has abstractyes

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