The influence of the quality of ferrosilicon on the rheology of dense medium and the ability to reach higher densities
Bibliographic record
Abstract
The dense medium beneficiation of heavy metal oxide ores such as iron ore is usually done by heavy medium separation in cyclones, static baths (Wemco drum) and nowadays with Larcodems. At Sishen Iron Ore Mine the first Larcodem was installed to beneficiate iron ore and a good comparison can be made between the Larcodem and Wemco drum after a three-year production period. A comparison will be made in this paper on the production performance of the Larcodem against the Wemco drum concentrating on the throughput and the high densities of 4.2 achieved. Performance problems encountered during the commissioning of the Larcodem and the rectification will be discussed as well as the production cost. The flow lines of the Larcodem module will be discussed and special emphases will be put on the changes made after commissioning to be able to keep the vortex in the Larcodem constant at a density of 4.2. All the advantageous and disadvantageous will be listed.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".