Efficient gold scavenging by iron sulfide colloids in an epizonal orogenic gold deposit
Bibliographic record
Abstract
Invisible gold hosted by pyrite represents a large proportion of gold resources worldwide. Gold is enriched in pyrite relative to hydrothermal fluids by five orders of magnitude, but controls on the hyperenrichment of gold in pyrite remain unclear. Here, we present the first micrometer- to nanometer-scale evidence for a colloidal iron sulfide phase that forms a precursor to pyrite and shows a remarkable capacity to scavenge gold from ore fluid. In chalcedony cement of breccia ores from the world-class Daqiao epizonal orogenic gold deposit, China, we find that numerous iron sulfide colloids, together with silica colloids and amorphous carbonaceous matter, formed from a highly supersaturated ore fluid in response to rapid fluid depressurization. Mechanisms that favor gold incorporation into iron sulfide colloids include large specific surface areas, abundant structure defects, negatively charged surfaces, high reactivity, and sequestration by silica colloids. These iron sulfide colloids subsequently aggregated and transformed to spherical cryptocrystalline pyrite aggregates. This process may be common during hydrothermal gold mineralization. Co-precipitation of amorphous carbonaceous matter plausibly enhanced the stability and dispersibility of iron sulfide colloids, facilitated sequestration of iron sulfide by silica, and adsorbed minor amounts of gold. Iron sulfide colloids represent a previously unrecognized mechanism for hyperenrichment of invisible gold in pyrite under rapidly changing and potentially non-equilibrium conditions. This study emphasizes that the iron sulfide colloids may be an important component of conceptual models for hydrothermal gold mineralization within Earth’s crust.
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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 | 0.000 | 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 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".