Influence of Pulsation Frequency in Iron Oxide Jigging
Bibliographic record
Abstract
The importance of pigments for the civilization is obvious and well documented.Although these materials have been discovered many years ago, research continues nowadays.Industries often require new shades, colours and more homogeneous and stable pigments.The selection of mineral pigments is of major importance to acquire high quality, colour, purity and mostly free of chemical contaminants, such as chemicals from froth flotation process.The region of Catalão, Brazil, has several minerals species in the site, including apatite, barite, magnetite, monazite, niobium, titanite and vermiculite.Nowadays phosphate, niobium and barite are economically exploited.For the production of these minerals, the magnetite is removed through magnetic separation and sent to a tailings dam.The aim of this study is to evaluate the possibility of producing iron oxide to be used as pigments as well as to evaluate the pulsation frequency influence in the jigging process.A Denver jig, in lab scale was used.Test were carried out using six different particle sizes and four pulsation frequencies, keeping the water flow rate fixed at 20 litres per minute.The results indicate that iron oxide production for pigments is viable from phosphate rock tailings, since it was possible to produce magnetite concentrated with grades over 90% and magnetite recovery around 40%.
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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) | 0.001 | 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".