Temporal Separation of W and Sn Mineralization by Temperature-Controlled Incongruent Melting of a Single Protolith: Evidence from the Wangxianling Area, Nanling Region, South China
Bibliographic record
Abstract
Abstract Tungsten and Sn display similar behavior during magmatic processes and are commonly associated spatially and genetically with highly evolved granites. Nonetheless, they typically form separate deposits, even if their associated granites have the same protolith. This separation may be due to the fractionation of the metals at the magmatic-hydrothermal transition or their differential mobility during partial melting of the metasedimentary protolith. If this separation occurred at the magmatic-hydrothermal transition, the ages of the W and Sn deposits would be very similar, whereas if it occurred during partial melting, the deposits are likely to have different ages because of the concentration of the metals in different magma batches and, in extreme cases, during different magmatic events. New age data from the Wangxianling ore field in the western part of the world-class Nanling W-Sn metallogenic province demonstrate that the W and Sn mineralization took place at different times. The W mineralization (219.5 ± 3.4 Ma) is related to Triassic granites (224.9–217.8 Ma), whereas the Sn mineralization is related to granites of Late Jurassic age (154.7 ± 1.1 Ma). This difference in ages rules out fractionation at the magmatic-hydrothermal transition as an explanation for the spatial separation of the W and Sn deposits and implies that the separation was due to differences in the mobility of W and Sn during partial melting. Both suites of granite originated from the partial melting of the same metasedimentary rocks, and both are reduced and highly evolved. The W granites, however, have a lower zircon saturation temperature (~750°C) than the Sn granites (~800°C), which indicates that the magma forming the W granites was mainly the product of muscovite-dehydration melting, whereas that forming the Sn granites was largely the result of biotite-dehydration melting. The different melting paths indicate that W released during muscovite breakdown dissolved in the magma, whereas Sn was sequestered by restite biotite. At the higher melting temperature, the residual W and Sn, released during the subsequent breakdown of biotite, dissolved in the magma. Thus, the magma that generated at low temperature was enriched in W, leading to subsequent W mineralization, whereas the magma that generated at high temperature was enriched in Sn and produced an Sn-mineralized granite. The whole-rock Sr-Nd isotope data for the Triassic W granites plot in the compositional field of the regional basement rocks and are consistent with partial melting of an orogenically thickened crust by internal heating in a collisional setting. In contrast, the Sr-Nd isotope data for the Late Jurassic Sn(-W) granites are displaced toward a mantle composition, likely reflecting contributions from mantle-derived material. Given the emplacement of many of the Late Jurassic Sn(-W) granites close to the Chenzhou-Linwu fault, we propose that this structure was the focus of decompression melting of the mantle and the injection of mantle-derived melts into the crust during the Late Jurassic, which supplied the additional heat for the melting at higher temperature needed to generate magmas enriched in Sn. This model, which is based on differences in the behavior of Sn and W during crustal melting, is potentially applicable to other Sn-W metallogenic provinces where Sn and W deposits are temporally separated.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".