Physical Separations for Rare-Earth Beneficiation of the Nechalacho Deposit
Bibliographic record
Abstract
The rare-earth elements (REEs) are strategic metals which are indispensable to the development of modern defence systems, electronic applications, and green technologies. The growing economic and strategic importance of these sectors, coupled with uncertainty in the global supply, has led to the development of many new deposits around the world. Many of these deposits, such as the Nechalacho deposit, are complex and contain multiple rare-earth element-bearing minerals (REMs) for which there is limited processing knowledge. This study explores a physical-separations-based flowsheet to beneficiate the Nechalacho deposit, which employs a spiral concentrator to preconcentrate the ore at a relatively coarse particle size (d80 = 120 μm), before further size reduction (d100 = 53 μm) and separation using a Mozley laboratory shaking table and two stages (low- and high-intensity) of magnetic separation. QEMSCAN was used to understand the effectiveness of each stage of separation and provide recommendations to improve the process. Although optimisation would be required, the results demonstrate that the physical-separations-based flowsheet could be an effective method of beneficiation.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".