Germanium enrichment in supergene settings: evidence from the Cristal nonsulfide Zn prospect, Bongará district, northern Peru
Bibliographic record
Abstract
Supergene nonsulfide ores form from the weathering of sulfide mineralization. Given the geochemical affinity of Ge to Si 4+ and Fe 3+ , weathering of Ge-bearing sulfides could potentially lead to Ge enrichments in silicate and Fe-oxy-hydroxide minerals, although bulk rock Ge concentrations in supergene nonsulfide deposits are rarely reported. Here, we present the results of an investigation into Ge concentrations and deportment in the Cristal supergene Zn nonsulfide prospect (Bongará, northern Peru), which formed from the weathering of a preexisting Mississippi Valley-type (MVT) sulfide deposit. Material examined in this study originates from drillcore recovered from oxidized Zn-rich bodies ~ 15–20 m thick, containing ~ 5–45 wt% Zn and Ge concentrations ~ 100 ppm. Microanalysis and laser ablation-ICP-MS show that precursor sphalerite is rich in both Fe (mean Fe = 8.19 wt%) and Ge (mean Ge = 142 ppm). Using the mineral geothermometer GGIMFis—geothermometer for Ga, Ge, In, Mn, and Fe in sphalerite—proposed by Frenzel et al. (Ore Geol Rev 76:52–78, 2016 ), sphalerite trace element data from the Cristal prospect suggest a possible formation temperature ( T GGIMFis ) of 225 ± 50 °C, anomalously high for a MVT deposit. Germanium concentrations measured in both goethite (mean values 100 to 229 ppm, max 511 ppm) and hemimorphite (mean values 39 to 137 ppm, max 258 ppm) are similar to concentrations measured in hypogene sphalerite. Additionally, the Ge concentrations recorded in bulk rock analyses of sphalerite-bearing and oxidized samples are also similar. A persistent warm-humid climate is interpreted for the region, resulting in the development of an oxidation zone favoring the formation of abundant Zn hydrosilicates and Fe hydroxides, both able to incorporate Ge in their crystal structure. In this scenario, Ge has been prevented from dispersion during the weathering of the Ge-bearing sulfide bodies and remains in the resultant nonsulfide ore.
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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 | 0.001 |
| 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".