Microthermometric, Laser Raman Spectroscopic, and Volatile-IonChromatographic Analysis of Hydrothermal Fluids in the PaleozoicMuruntau Au-Bearing Quartz Vein Ore Field, Uzbekistan
Bibliographic record
Abstract
Fluid inclusions in quartz and scheelite from flat, steeply dipping central, and stockwork-type quartz veins within the Muruntau Au-bearing quartz vein ore field have been investigated in a reconnaissance study, using fluid inclusion mapping, microthermometry, laser Raman spectroscopy, and integrated gas and ion chromatography for bulk volatile and cation-anion analysis. Muruntau central veins are dominated by inferred early CO2-bearing fluid inclusions. In contrast, flat quartz veins contain considerable numbers of low-density, pure aqueous inclusions on deformation- and recrystallization-related microstructures. Fluid phase separation is indicated for samples from the Muruntau central ore veins by fluid inclusion observational work, as well as by microthermometry and bulk fluid geochemistry (CO2/CH4, CO2/N2, and CO2/C2-, and C3- hydrocarbon ratios; salinity data). However, in flat veins and all samples from the Myutenbai deposit no evidence for fluid immiscibility could be found. Quartz microstructural results and fluid inclusion data suggest formation of the low-grade mineralized flat veins before the main stage of hydrothermal activity and considerable variation in the geochemical conditions during fluid evolution in the Muruntau and Myutenbai deposits. Mixing of fluids from different sources in variable proportions may be inferred from halide geochemistry. Furthermore, fluid inclusion Br/Cl ratios differ significantly for samples from different vein types. Fluid phase separation is suggested as a mechanism for the precipitation of Au from the hydrothermal fluid in the Muruntau high-grade Au mineralized central veins.
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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 | 0.000 |
| 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 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".