Evolution of Non-Metallic Inclusions in Secondary Steelmaking: Learning from Inclusion Size Distributions
Why this work is in the frame
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Bibliographic record
Abstract
Non-metallic inclusions have always been the active subject of steelmaking research to improve the steel cleanliness and to develop the so-called oxide metallurgy technology. Inclusions in molten steel form and grow by the sequence of nucleation, chemical and physical growth and removal. Thus, the size distribution of inclusions evolves continuously with time in molten steel, and significant changes in the steel conditions are reflected in the inclusion size distribution as well as in the inclusion chemistry. This study aims to provide a new approach to interpret the inclusion size distributions. The concept of the Population Density Function (PDF) is introduced to objectively represent a given inclusion size distribution. Several possible applications of PDF analysis are presented to demonstrate the advantages of the utilization of the PDF for understanding the inclusion formation mechanism during the steelmaking process. Several ambitious ideas to utilize the PDF for inclusion size control are also presented.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it