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Interaction between Steel and Distinct Gunning Materials in the Tundish

2014· article· en· W1977977964 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

VenueISIJ International · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsTundishMetallurgyMaterials scienceRefractory (planetary science)Infiltration (HVAC)Composite materialContinuous casting

Abstract

fetched live from OpenAlex

Gunning materials are used as a dispensable protective layer to extend the tundish life. Their use however, affects the steel cleanliness by introducing steel reoxidation and exogenous inclusions. In the present work, the interaction between molten steel and three types of refractory, i.e. MgO, Al2O3, and MgO + 2MgO∙SiO2 based gunning materials (GM) have been investigated. Compared to MgO and MgO + 2MgO∙SiO2 GM, the Al2O3 GM exhibited better infiltration resistance to molten steel. The two phase MgO + 2MgO∙SiO2 GM was found to be less prone to steel infiltration than single-phase MgO GM. The compositional evolution of steel influenced by gunning material was experimentally measured and also predicted with thermodynamic equilibrium calculations. The steel cleanliness in terms of inclusion size, number density, area fraction and morphology was measured and evaluated. Although large Al2O3/AlTiOX inclusions were formed through the erosion of Al2O3 GM, the use of Al2O3 GM resulted in an improved steel cleanliness due to its lower content of reducible compounds compared to that of MgO and MgO + 2MgO∙SiO2 GM.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.178

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 studies0.0000.000
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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