Identification of Zn-Bearing Micas and Clays from the Cristal and Mina Grande Zinc Deposits (Bongará Province, Amazonas Region, Northern Peru)
Bibliographic record
Abstract
Zn-bearing phyllosilicates are common minerals in nonsulfide Zn deposits, but they seldom represent the prevailing economic species. However, even though the presence of Zn-bearing clays is considered as a disadvantage in mineral processing, their characteristics can give crucial information on the genesis of the oxidized mineralization. This research has been carried out on the Mina Grande and Cristal Zn-sulfide/nonsulfide deposits, which occur in the Bongará district (Northern Peru). In both of the deposits, Zn-bearing micas and clays occur as an accessory to the ore minerals. The XRD analyses and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) investigations revealed that the Zn-bearing micas that are occurring in both deposits mostly consist of I/S mixed layers of detrital origin, which have been partly altered or overprinted by sauconite during the supergene alteration of sulfides. Sporadic hendricksite was also identified in the Cristal nonsulfide mineral assemblage, whereas at Mina Grande, the fraipontite-zaccagnaite (3R-polytype) association was detected. The identified zaccagnaite polytype suggests that both fraipontite and zaccagnaite are genetically related to weathering processes. The hendricksite detected at Cristal is a product of hydrothermal alteration, which is formed during the emplacement of sulfides. The complex nature of the identified phyllosilicates may be considered as evidence of the multiple processes (hydrothermal and supergene) that occurred in the Bongará district.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".