The role of phyllosilicate partial melting in segregating tungsten and tin deposits in W-Sn metallogenic provinces
Bibliographic record
Abstract
Abstract Most tungsten (W) and tin (Sn) deposits are associated with highly evolved granites derived from the anatexis of metasedimentary rocks. They are commonly separated in both space and time, and in the rare cases where the W and Sn mineralization are part of a single deposit, the two metals are temporally separate. The factors controlling this behavior, however, are not well understood. Our compilation of whole-rock geochemical data for W- and Sn-related granites in major W-Sn metallogenic belts shows that the Sn-related granites are generally the products of higher-temperature partial melting (~800 °C) than the W-related granites (~750 °C). Thermodynamic modeling of partial melting and metal partitioning shows that W is incorporated into the magma formed during low-temperature muscovite-dehydration melting, whereas most of the Sn is released into the magma at a higher temperature during biotite-dehydration melting; the Sn of the magma may be increased significantly if melt is extracted prior to biotite melting. At the same degree of partial melting, the concentrations of the two metals in the partial melt are controlled by their concentration in the protolith. Thus, the nature of the protolith and the melting temperature and subsequent evolution of the magma all influence the metallogenic potential of a magma and, in combination, helped control the spatial and temporal segregation of W and Sn deposits in all major W-Sn metallogenic belts.
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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 | 0.001 | 0.001 |
| 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".