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Dimensionless numbers for tundish modelling and the Guthrie number ( <i>Gu</i> )

2012· article· en· W2053822901 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIronmaking & Steelmaking Processes Products and Applications · 2012
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsTundishSimilarity (geometry)Dimensionless quantityDynamic similarityScale (ratio)Flow (mathematics)Heat transferMathematicsComputer scienceMechanicsEngineeringMechanical engineeringGeometryPhysicsArtificial intelligenceReynolds number

Abstract

fetched live from OpenAlex

Modelling the transport phenomena in tundishes has been a vast area of research for the last three decades. Many papers have been published and are available in the literature on this subject. The basics of modelling involve a similarity criterion between the model and the full scale prototype and are well documented in major textbooks. However, the similarity criteria are different for different cases. For example, for fluid flow in a tundish, the Re and Fr similarity is considered, whereas for heat transfer, the Pr and Pe number similarity should be considered. Numerous other examples can be cited. It is really important to know which similarity criteria should be used for a particular case. In this paper, a new dimensionless number Gu has been proposed when dealing with the modelling of inclusions separating out in a tundish. All dimensionless numbers can be represented as a ratio of two characteristic time scales, and this fact is highlighted in the present paper.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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