RECOGNITION OF A 600-KM-LONG LATE TRIASSIC RARE METAL (Li-Rb-Be-Nb-Ta) PEGMATITE BELT IN THE WESTERN KUNLUN OROGENIC BELT, WESTERN CHINA
Bibliographic record
Abstract
Abstract The rising demand of strategic metals, especially lithium, necessitates discovery of new resources to meet the global supply chain. Recently, several pegmatite-hosted rare metal (Li-Rb-Be-Nb-Ta) deposits have been discovered in the Western Kunlun orogenic belt, making it a new world-class rare metal resource (estimated ~7 Mt Li2O and 0.16 Mt BeO). Understanding the metallogenesis of this belt is critical to further evaluate the rare metal potential. In this study, columbite-tantalite (coltan) and monazite from rare metal pegmatites and zircon from potential parental granites were collected from five representative rare metal pegmatite deposits in the western, middle, and eastern parts of the Western Kunlun orogenic belt for U-Pb geochronology. The results indicate that despite the distances of the sampling localities in different parts of the Western Kunlun orogenic belt, the ages of pegmatite-hosted rare metal mineralization fall in a narrow range of ca. 208–204 Ma. These rare metal pegmatites are temporally and spatially related to adjacent postorogenic granites emplaced following the closure of the Paleo-Tethys Ocean. The compositional characteristics of K-feldspar, biotite, and muscovite of the granites and pegmatites, along with regional mineralogical and textural zonation of the pegmatites, suggest that the rare metal pegmatites were derived from the volumetrically much more important, highly fractionated granitic intrusions. We propose that, in combination with the data from previous studies, the 218–204 Ma interval represents a newly recognized rare metal metallogenic period linked with granitic intrusions in the Western Kunlun orogenic belt, revealing a 600-km-long late Triassic rare metal pegmatite belt composed of multiple ore fields formed in a similar metallogenic setting. These results emphasize the importance of identifying fertile, Late Triassic to Early Jurassic granitic intrusions for rare metal pegmatite exploration. Furthermore, combined with recent studies on the Songpan-Ganzi rare metal pegmatite belt along the eastern segment of the Paleo-Tethys, this study further highlights the great potential of rare metal resources in this global tectonic zone.
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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 | 0.001 |
| 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) | 0.001 | 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".