Controls on the Dynamics of Rare Earth Elements During Subtropical Hillslope Processes and Formation of Regolith-Hosted Deposits
Bibliographic record
Abstract
Abstract Subtropical weathering of granitic catchments in South China has led to the formation of numerous giant regolith-hosted rare earth element (REE) deposits that currently account for more than 15% of global REE production and more than 95% of global heavy REE (HREE) production. Understanding the controls on mobilization and redistribution of the REEs during subtropical weathering in these granitic catchments is crucial for efficient exploration for this type of deposit in the world. As exemplified by the Bankeng light REE (LREE) deposit in South China, the key factors controlling the mobilization and redistribution of the REEs, especially the easily exchangeable REEs, are soil pH and primary REE mineralogy. The nature of the primary REE minerals, apatite, monazite-(Ce), and subordinate bastnäsite-(Ce), parisite-(Ce), and xenotime-(Y) places an important control on the behavior of the REEs during incipient weathering. Dissolution of these minerals is slow during incipient weathering, and, therefore, enrichment in REEs in this stage results largely from the removal of major elements during the decomposition of albite, K-feldspar, and biotite. Dissolution of the primary REE minerals higher in the profile liberates the REEs, which are then transported to locations where the soil pH abruptly increases due to water-regolith interaction, such as the pedolith-saprolite interface, and adsorption on kaolinite-group minerals efficiently fixes the REEs in regolith. Geomorphologically, the Bankeng deposit, like most of the other regolith-hosted REE deposits in South China, is located on concave-convex hillslopes, where erosion is prevalent at the ridgetop and decreases in intensity downslope. Results of this study show that strong erosion, coupled with intense chemical weathering at the ridgetop, is responsible for the enrichment in REEs by releasing the REEs, especially the LREEs, from their primary sources and supplying kaolinite and halloysite needed for the REE adsorption by decomposing albite, K-feldspar, and biotite. Decomposition of these major rock-forming minerals also leads to an enrichment of the REEs through the removal of components. The HREEs are lost preferentially to the groundwater and transported downslope, resulting in the enrichment of these elements in the lower part of the weathering crust at the footslope. Significant lateral Ce transport is also probable. A series of oxic fronts were developed at the footslope, with the most persistent one along the saprolite-saprock interface, due to seasonal fluctuations of the groundwater table. Cerium was immobilized there, predominantly through adsorption on Fe-Mn oxyhydroxides, causing enormous accumulation. Therefore, hillslope processes and groundwater flow could redistribute the REEs across the entire catchment, preferentially enriching the LREEs at the ridgetop and the HREEs at the footslope. Also, intense erosion facilitates chemical weathering and the accumulation of REEs, but the development of a thick weathering crust is favored by weak erosion. Repeated periods of high and low erosion rates in South China have enabled the gradual development of thick weathering crusts at the ridgetops that are sufficiently enriched in REEs to now constitute a major resource of these economically important elements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".