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.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".