The Origin of Native Metal-Arsenide Mineralization in the World-Class Schlema-Alberoda Uranium Deposit (Germany): Insights from Arsenide Compositions and Fluid Inclusion Characteristics
Bibliographic record
Abstract
Abstract The Schlema-Alberoda deposit in the West Erzgebirge region of Germany was one of the largest uranium deposits (extraction of 80 kilotonnes [kt] U) in central and western Europe. It is also a prime example of post-Variscan native metal-arsenide mineralization that is closely associated with uranium mineralization. This study focuses on the nature and composition of native metal-arsenide associations that occur as high-grade ore shoots across the Schlema-Alberoda deposit. Fluid inclusions from gangue minerals genetically related to the native metal-arsenide associations have homogenization temperatures between 126° and 138°C and fluid salinities of ~24.4 to 27.3 wt % (NaCl + CaCl2 equivalent). Fluid inclusion volatiles hosted in gangue minerals indicate that sedimentary and basement fluids mixed during arsenide formation. Fluid mixing occurred in response to the injection of a deep-seated metal-bearing basement fluid into shallower aquifers, triggered by progressive crustal thinning during the Mesozoic. Reduction of these low-temperature and high-salinity basement fluids by carbonaceous rock types is interpreted to have led to the formation of high-grade Co-Ni-Fe-arsenide ore shoots at Schlema-Alberoda. Mineralogical and petrographic observations document a distinct temporal zonation from nickel- and cobalt-rich to cobalt-iron–rich arsenide minerals. There is, however, no evidence of spatial mineralogical zonation on the vein and deposit scale. Nonetheless, skutterudite and nickelskutterudite decrease in S and increase in Fe contents with depth and decreasing distance to the redox barrier. Hence, we propose that the S and Fe concentration of the triarsenides could be a useful vector toward the redox front, which constrains the lower depth limit of mineralization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".