The role of fluorine in granite-related hydrothermal tungsten ore genesis: Results of experiments and modeling
Bibliographic record
Abstract
Most of the World’s large tungsten deposits are genetically related to fluorine-rich granitic magmas, their ores contain fluorine minerals, such as fluorite and topaz, and their host rocks have commonly undergone fluorine metasomatism to form greisens. In view of this evidence that tungsten ore fluids are enriched in fluorine, and the potential for fluoride to form stable complexes with hard acids (e.g., W6+), we investigated the solubility and speciation of tungsten in fluoride-bearing fluids at hydrothermal conditions. Experiments were conducted to measure the solubility of tungsten trioxide solid in fluoride-bearing aqueous fluids at temperatures between 100 and 250 °C and vapour-saturated water pressure. Based on the results of these experiments, tungsten is dissolved predominantly as H3WO4F2− in solutions containing 0.05–0.30 m HF at pH(T) values between 1.5 and 3.4. Significantly, the concentration of H3WO4F2− was up to two orders of magnitude higher than that of tungstate species in these experimental solutions. This species formed via the reaction WO3cryst + H2O + 2HF = H3WO4F2− + H+, logarithms of the equilibrium constants for which, vary from −4.39 ± 0.18 at 100 °C to −5.10 ± 0.17 at 250 °C. In solutions containing <0.05 m HF, the dominant species is H2WO40. Fluid-rock interaction models employing these data predict that fluorine-bearing fluids will deposit ten times the mass of tungsten at a given fluid-rock ratio than fluorine-free hydrothermal fluids (the latter models consistently underestimate tungsten deposit grades). They also successfully explain why wolframite and not scheelite is the major tungsten mineral in vein- and greisen-type granite-hosted deposits, showing that calcium released to the fluid is consumed to form fluorite, thereby precluding saturation of the fluid with scheelite. The occurrence of fluorite in tungsten deposits is therefore not coincidental but instead a byproduct of the mineralizing process.
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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".