Application of multiple linear regression (MLR) analysis on the concentration of chromite plant tailings by a shaking table
Bibliographic record
Abstract
In this paper, the beneficiation of the Upper Group 2 (UG-2) plant tailings was investigated to recover chromite.Multiple linear regression analysis was applied to estimate the recovery and grade of chromite.A detailed chemical and mineralogical characterization of the UG2 plant tailings was conducted using X-ray fluorescence (XRF) and Scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) for elemental composition and surface morphology respectively.The results showed that the major elements in the chromite plant tailings are Fe (12.96 wt%), Cr (10.97 wt%) and Si (15.64 wt%).The major mineral phases are quartz, spinel, magnesiochromite and aluminosilicates.The results attained from the Microtrac particle analyser indicated that 80% of the particles are < 95 μm and only 6% was less than 10µm.The highest recovery of Cr2O3 was found to be 67.27% at a grade of 21.87% chromite.Multiple linear regression equations were created based on the experimental results to forecast the recovery and grade of the chromite concentrate, and the regression coefficients between experimental and anticipated values were poor for grade and good for recovery (R2 values of 0.18 and 0.66, respectively).It was concluded that the generated MLR equations can be used to forecast the recovery of chromite from UG-2 plant tailings.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".