An overview of rare-earth recovery by ion-exchange leaching from ion-adsorption clays of various origins
Bibliographic record
Abstract
Abstract Continuous development of advanced technologies has created increasing demand for rare-earth elements ( REE ), with global emphasis on identifying new alternate sources to ensure adequate supply. Ore deposits containing physically adsorbed lanthanides are substantially lower grade than other REE deposit types; however, the low mining and processing costs make them economically attractive as sources of REE . To evaluate the commercial potential for the recovery of REE s from ion-adsorption deposits in a systematic manner, a standardized procedure for REE leaching was developed previously. Using this procudure it was found that, regardless of variations in ore origin and REE content, all REE consistently reached peak extraction levels under ambient conditions with fast kinetics. Various techniques to improve the REE extraction through process variations were also investigated: it was found that decreasing the L:S ratio, re-using leachate on fresh ores and counter-current leaching were all capable of increasing REE concentrations in the resultant leachate, albeit at the expense of REE extraction levels. In addition, the water content trapped in the leached material was found to contain significant amounts of REE and residual lixiviant requiring thorough washing of the solid residue.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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".