Research progress on prediction of FeO content in sinter based on intelligent algorithm
Bibliographic record
Abstract
The FeO content of sinter is closely related to the drum strength and reduction performance of sinter and reflects the heat control level of the sintering process. It is one of the key indicators to measure the quality of sinter and the level of production operation. However, due to the large lag of sintering process and quality detection, the detected FeO content can only reflect the production state several hours ago, so the early prediction of FeO content is very important. In this paper, the theoretical basis of FeO content prediction is expounded from the aspects of FeO formation mechanism, influencing factors and prediction difficulties of sinter. The advantages, disadvantages and applicable scenarios of traditional FeO detection methods are compared and analysed. Then, the evolution, application and latest progress of the prediction technology of FeO content in sinter are summarised from the perspectives of process parameters and tail section, and the technical bottleneck and future direction of FeO content prediction are pointed out.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".