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Record W2601171488 · doi:10.1080/03019233.2017.1305676

Distribution of macro-inclusions in low carbon aluminium-killed steel slabs

2017· article· en· W2601171488 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 · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersUniversity of Science and Technology Beijing
KeywordsSlabMaterials scienceContinuous castingInclusion (mineral)AluminiumCarbon steelNozzleComposite materialMachiningMetallurgyGeologyMineralogy

Abstract

fetched live from OpenAlex

Macro-inclusions in low carbon, aluminium-killed steel slabs were characterised by step-machining within a 10 mm zone from the slab surface using an ASPEX automatic inclusion analyzer. Dendritic structures within the cross-section of slabs were examined. The results show that alumina clusters and alumina associated with bubbles are the dominant macro-inclusions. Along the slab width direction, macro-inclusions were mostly found at the slab centre because of the deeper hooks and freezing meniscus surrounding the submerged entry nozzle. In terms of slab thickness, inclusions were mainly concentrated within the zone 3.5–6 mm from the top of the slab surface, where the columnar dendrites showed a relatively small inclination angle, indicating small cross-flow velocities at the solidification front. The number density of macro-inclusions were strongly dependent on the washing effect produced by the flow velocity. High speed casting promotes this behaviour and improves the surface quality of the slabs.

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 score0.449
Threshold uncertainty score0.975

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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