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Record W2057113761 · doi:10.1179/030192302225003486

Slag entraining vortexing funnel formation during ladle teeming: similarity criteria and scale-up relationships

2002· article· en· W2057113761 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 · 2002
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsLadleTundishEntrainment (biomusicology)Slag (welding)SteelmakingPhysical modellingFunnelEngineeringScale (ratio)Dimensionless quantityMechanical engineeringYield (engineering)Process engineeringMechanicsMetallurgyMaterials scienceNozzleGeotechnical engineering

Abstract

fetched live from OpenAlex

There are several motivations for minimising slag entrainment during the teeming of steelmaking ladles. Cleaner steel, improved yield, and higher productivity are all at stake. As one of several identifiable contributors to slag entrainment, vortexing has received considerable attention in the past decade and a half. What is commonly referred to as 'vortexing' in fact comprises two distinct phenomena, namely, vortexing funnels and non-vortexing funnels, each controlled by entirely different sets of variables. Dimensionless correlations describing the two phenomena were determined, and validated, using separate sets of dimensional analyses and appropriately designed scale model experiments. The importance of these findings to the teeming of steel is discussed. Performance results of a patented 'vortex buster' device, developed on the basis of the understanding gained from these studies, and validated in water models as well as in a 12 ton tundish, are also presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.223
Teacher spread0.190 · 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