NORTH AMERICAN STEELMAKING SLAGS-A SOURCE FOR CRITICAL ELEMENTS
Bibliographic record
Abstract
The need for critical minerals will continue to rise as the world population continues to grow and the world strives to limit global warming. Countries determine their critical minerals lists based on the minerals and elements that are sensitive to supply chain disruptions or are in limited supply. The traditional source for critical minerals has been mineral deposits. These deposits take decades to bring into production and hundreds of millions of dollars. There is an advantage to sourcing these elements from waste due to the immediate availability of the waste and the relatively inexpensive cost to obtain it. Steelmaking slag is a waste type that is produced as steel is made. This study shows that the North American steelmaking slag analyzed contains 17 critical elements (Al, Ba, Co, Cr, Cu, Mg, Mn, Mo, Ni, P, Sb, Sc, Ti, V, W, Zn, Zr) as well as 9 of the rare earth elements (Dy, Eu, Gd, La, Lu, Pr, Tb, Y, Yb). Recovering value from steel slags is an underexplored area of research. Extraction techniques include pyrometallurgy, hydrometallurgy, and biohydrometallurgy. Biohydrometallurgy looks to be a promising extraction technique from cost and environmental perspectives. As long as steel is produced, there will be a source of steelmaking slag, which makes this type of slag waste a "renewable" resource for critical elements.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".