The hyper-enrichment of silver through the aggregation of silver sulfide nanoparticles
Bibliographic record
Abstract
Silver deposits have long been considered to form due to the direct precipitation of silver minerals from aqueous fluids, in which the metal is transported as chloride and/or bisulfide complexes. Ultra-high-grade silver ores have silver contents up to tens of weight-percent in the form of silver sulfides and native silver. Ore-forming fluids of most silver deposits, however, typically contain low silver contents of parts per million silver. The challenge is to explain how fluids with such low concentrations of silver can form ultra-high-grade silver ores. Here, we present direct mineralogical evidence from natural samples showing that the high-grade silver ores form from the aggregation of silver sulfide nanoparticles through intermediate microparticles and dendrites to acanthite crystals. Native silver grows from silver sulfides via solid-state silver ion aggregation. This study traces the formation of silver sulfides from their nanoparticulate precursors, thereby providing insights into the genesis of ultra-high-grade silver ores in a variety of metallogenic settings. This study traces a pathway for the natural formation of silver sulfide and silver wire through the coalescence of silver sulfide nanoparticles and solid-state silver ions aggregation that is responsible for high-grade silver deposits
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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) | 0.001 | 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".