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Fluid Slag Skimming from Steel Ladles.

2001· article· en· W2063847621 on OpenAlex
K. W. Ng, R. Harris

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 · 2001
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsLadleSteelmakingSlag (welding)MetallurgyWater modelMaterials scienceReynolds numberLiquid steelMechanicsChemistryTurbulence

Abstract

fetched live from OpenAlex

A 1/7 scale model was constructed to physically simulate the skimming process of desulphurization slag from the transfer ladle used in the steelmaking industry. 1-decanol and water were used to represent the slag and the underlying molten steel, respectively. It was observed that the underlying liquid surface circulation created by three impinging gas jets in glancing contact with the bath surface would carry the slag towards the skimming mouth. The Reynolds number of the jets (I.D. 0.01 m) inclined 50° to the horizontal was 6100. In the same amount of time, 70% of the slag originally charged to the tank was skimmed with the assistance of the jets in comparison to 20 % when no jets were used. This approach to slag flow control was considered much more effective and less generally damaging than the use of submerged gas injection.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.998

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)0.0030.000

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 head0.008
GPT teacher head0.209
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