Paleoproterozoic gold deposits at Alta Floresta Mineral Province, Brazil: two overprinted mineralizing events?
Bibliographic record
Abstract
Abstract Large gold provinces commonly show complicated mineralization histories, and the Paleoproterozoic Alta Floresta, one of Brazil's most exciting Au–Cu mineral provinces, is a good example. The current models defined four deposit types, all connected to a single (1.88–1.75 Ga) magmatic–hydrothermal event. However, long Province history, diverse geodynamic environment and older ages of Type-1mineralization weaken the single metallogenic event and enable the hypothesis of overprinted mineral events. By scale-integrated analyses, we revise the tectonic–geological context, structural–hydrothermal alterations and chlorite–white mica geothermobarometry and propose the Type-1 as an older, granitoid-hosted orogenic mineralization, with subsequent overprinting by the magmatic–hydrothermal event. The older orogenic gold event developed orogenic gold deposits on WNW-trending shear zones in the Peixoto de Azevedo domain granitic–gneiss rocks. Phengite, biotite and chlorite–carbonate phyllonites (3.3–6.1 kbar, 300–420°C) host fault-fill quartz veins (pyrite–chalcopyrite–magnetite–pyrrhotite–gold–Bi–Ag tellurides). Mg-rich chlorite–phengite is the main alteration footprint for this mineralization type. A younger magmatic–hydrothermal event in the Juruena magmatic-arc rocks produced Fe-rich chlorite–white mica alteration zones (0.6–4.6 kbar, 120–380°C) and disseminated and stockwork–breccia ore (pyrite–chalcopyrite–gold–molybdenite–Ti minerals–allanite) in porphyry–epithermal deposits. Where the younger mineralization overprints the older, phyllic alteration destroyed the phengite orogenic gold phyllonite S n +1 foliation. The ages of two pyrite populations (1979 and 1841 Ma) in the older fault-fill veins and molybdenite in late fractures (1805–1782 Ma) or disseminated in the c. 1.79 Ga syenogranite porphyry suggest more than two episodes of mineralization. These two events differ in their alteration styles, P–T conditions and structural, mineralogical and textural ore styles. The multiscale approach sheds light on the relationships between the various mineralization events, allowing a new explorational potential within the province. Supplementary material: Chlorite and white mica complete chemical composition data and other characteristics of the studied deposits are available at https://doi.org/10.6084/m9.figshare.c.6056324
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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 | 0.001 |
| 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".