Protolith-Related Thermal Controls on the Decoupling of Sn and W in Sn-W Metallogenic Provinces: Insights from the Nanling Region, China
Bibliographic record
Abstract
Abstract The Nanling region of South China hosts the largest W-Sn metallogenic province in the world, accounting for more than 54% of global tungsten resources as well as important resources of tin and rare metals. An important feature of this province, which is shared by a number of other W-Sn metallogenic provinces, is that W deposits occur separately from Sn and Sn-W deposits, with the latter concentrated in the western part of the region (especially along the deep, NE-trending Chenzhou-Linwu fault) and the W deposits to the east of them. All the deposits are associated with ilmenite series, peraluminous granites. However, the granites associated with the Sn and Sn-W deposits can be distinguished from the W granites by their higher bulk-rock εNd values and their higher zircon εHf values. Most importantly, the Sn and Sn-W granites are characterized by higher zircon saturation temperatures (800 ± 20°C) than the W granites (650°–750°C). The Sn and Sn-W granites also contain abundant mantle-derived mafic microgranular enclaves, whereas such enclaves are rare in the W granites. A model is proposed in which the protolith to the W granites released W to the melt as a result of the breakdown of muscovite. The temperature of melting, however, was too low for biotite to melt. In the west, particularly along the Chenzhou-Linwu fault (the location of the Sn and Sn-W deposits), higher temperatures enabled the breakdown of both muscovite and biotite and the consequent release of both Sn and W to form Sn and Sn-W granites. This model, which is based on differences in the protolith melting temperature and thus mobilization temperatures for Sn and W, is potentially applicable to any Sn-W metallogenic province in which the Sn and Sn-W deposits are spatially separated from the W deposits.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".