Quality Improvement of Iron Ore Jasper by Selective Milling
Bibliographic record
Abstract
One of the most important factors during the process of comminution of minerals is their power consumption rate, which is determined by calculating the WI (work index), which in short is to analyze the amount of kWh consumed per ton of material, to reach a certain particle size and achieve the desired. Along with the quality of the ore is defined and use the classification of the ore. Ores of jaspelite type, high hardness, and with lower iron content 60% with silica content above 10.5%, tends to be considered economically unsuitable for merger cases in the Brazilian market. The work presented here consists of a technique that acts by transforming this type of ore at an acceptable quality product and with a lower power consumption than the previously calculated by WI analysis. The procedure presented here recovers a quantity of more than 75% by weight, taking an ore 56% Fe, for an average content of 65% SiO2 and lowering the 10% to 4.5%. Although reducing by 50% the amount of phosphorus present. The procedure presented here using known methods, but with a variation with respect to the operation, which gives you innovative character, acting together with a selective screening.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".