Features of using INSTEEL®modifiers. Industrial testing.
Bibliographic record
Abstract
Producing metal with low non-metallic inclusion contamination remains a pressing challenge for metallurgical engineers. One key area for improving steel refining technology is selecting optimal compositions of refining alloys to enhance secondary metallurgy efficiency. This study presents results from using cored wire containing alkaline earth metals (Ca, Ba) at large-scale steelmaking plants. The research highlights the mechanism of non-metallic inclusion removal when using alkaline earth metal complexes compared to conventional calcium-based materials. Notably, the SiCaBa modifier demonstrated significantly improved calcium yield stability, enabling more reliable prediction of residual calcium content in molten steel. This helps prevent calcium overmodification and reduces risks of additional non-metallic inclusion formation. Industrial trials of INSTEEL® cored wire under various production conditions showed consistent trends: reduced non-metallic inclusion contamination; increased calcium yield efficiency (29.9% at Ural Steel JSC, 14.6% at KSP Steel LLP, and 23.5% at EVRAZ NTMK JSC); improved cleanliness ratings: at Ural Steel JSC (17G1SU grade): Brittle silicate max score decreased from 4.0 to 2.5; non-deformable silicates from 4.0 to 3.0; at EVRAZ NTMK JSC (OS grade). Non-deformable silicate contamination reduced from 2.75 to 2.47; 12.9% reduction in refining treatment costs. The findings demonstrate that complex alkaline earth metal alloys offer measurable advantages in secondary metallurgy processing compared to standard calcium treatments
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".