MECHANISMS OF CRYSTAL GROWTH AND ZONING OF HYDROTHERMAL PYRITE FROM Sb-Au DEPOSITS IN NIZKE TATRY, SLOVAKIA
Bibliographic record
Abstract
Pyrite is one of the most common ore minerals in hydrothermal ores and a hallmark of many types of hydrothermal deposits, e.g. , mesothermal quartz veins with pyrite, arsenopyrite, and visible or invisible gold. In addition to any other mineralogical investigation, it is possible to examine and interpret the crystal size distribution (CSD) of the mineral which is under investigation. The conditions of mineral-forming processes can be diverse in terms of temperature, pressure, and compositional variables, but the CSD shapes are usually convergent and by far the most common CSD type is the lognormal distribution. In this study, we determined the CSD for pyrite crystals from mesothermal quartz-pyrite-arsenopyrite-stibnite-gold ores from the Nizke Tatry Mountains (Western Carpathians, Slovakia). A large number of crystals were measured manually from ore sections or from stereographic images. All samples show a crystal size spectrum from ≈2 μm up to ≈550 μm. We discovered that all CSDs show a lognormal or pseudo-lognormal shape with low logarithmic size variances (β2). In order to understand the history of nucleation and growth of pyrite formed in hydrothermal milieus, we compared our measurements to simulations with different growth mechanisms using the GALOPER (Growth According to the Law of Proportionate Effect) program (after [Eberl et al. 1998][1]). From the shape of the distributions, the logarithmic mean (α) and β2, we inferred that the crystals grew in an open system with its associated growth mechanisms (surface-controlled growth and supply-controlled growth), since no evidence for ripening processes in the data or in the numerical simulations of our data could be found. Since ripening as a growth mechanism can be excluded, we have to assume that the growth of the pyrite crystals and so the formation of the whole ore body occurred on a relatively short geological time scale. This preliminary result opens more questions related to the emplacement, circulation, and supersaturation of hydrothermal fluids and to the formation of large ore bodies over short time scales. [1]: #ref-10
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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".