Optimization of agglomeration burdens by metallugical properties complex. Report 1. Optimization of agglomeration burdens by technological characteristics of sinter production
Bibliographic record
Abstract
Despite a lot of studies of iron ore raw materials was carried out both in sinter and BF production areas, the matter of agglomeration burdens optimization is still actual. Laboratory studies on sintering of agglomeration burdens of different component content were carried out for optimization of iron ore burden content optimization, following by determination of technology characteristics and metallurgical properties complex. As a result of the studies an optimal component and size content of the agglomeration burden determined to provide improving of metallurgical properties complex of agglomeration burden. The studies carried out showed, that lime introducing into the concentrate flow before the burden department can lead to sintering machines productivity increasing. The burden wetness range determined, enabling for complete lime hydrating. It was shown, that a partial replacement of agglomeration ores in the burden by BOF nickel slag contributes to agglomeration process specific productivity increasing as well as sinter strength increasing.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".