Hydrothermal alteration of magmatic titanite: Implications for REE remobilization and the formation of ion-adsorption HREE deposits, South China
Bibliographic record
Abstract
Abstract Ion-adsorption rare earth element (REE) deposits in South China are currently the main source of heavy rare earth elements (HREE). The Gucheng deposit in western Guangdong Province is one example of HREE mineralization hosted in weathered coarse-grained biotite granites (CGBG). Titanite is a common accessory mineral in the CGBG and contains significant amounts of total REE (31 621 to 38 431 ppm), especially HREE (18 906 to 22 249 ppm). Titanite with a U-Pb age of 102.6 ± 1.9 Ma in the CGBG crystallized under relatively high temperatures (722–798 °C), high fH2O, and high fO2 conditions in the late magmatic stage, and has similar Nd isotopic compositions similar to the host CGBG: 143Nd/144Nd = 0.512062 to 0.512125 and εNd(t) = –7.4 to –8.6. Backscattered electron (BSE) imaging and TESCAN integrated mineral analyzer (TIMA) measurements show that titanite in the CGBG has been altered partly to fergusonite-(Y), rutile, calcite, quartz, and fluorite. The hydrothermal fluid responsible for titanite alteration was enriched in CO32− and F, and was probably exsolved from the granitic magma. HREE released from the alteration of titanite were mostly scavenged by fergusonite-(Y) and rutile, which have been further replaced by gadolinite-(Y) and synchysite-(Ce). In addition, gadolinite-(Y) in the alteration assemblages exhibits further alteration and is characterized by elevated PO43− and SO42− contents in the altered parts. These results demonstrate that magmatic titanite in the CGBG underwent complex hydrothermal alteration, with a preferential accumulation of HREE in fergusonite-(Y) and gadolinite-(Y) in the alteration assemblages. Preferential HREE enrichments in magmatic titanite, and its alteration assemblages, are shown to play significant roles in the formation of the Gucheng HREE deposit.
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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".