Extreme shifts in pyrite sulfur isotope compositions reveal the path to bonanza gold
Bibliographic record
Abstract
Pyrite is the most common sulfide mineral in hydrothermal ore-forming systems. The ubiquity and abundance of pyrite, combined with its ability to record and preserve a history of fluid evolution in crustal environments, make it an ideal mineral for studying the genesis of hydrothermal ore deposits, including those that host critical metals. However, with the exception of boiling, few studies have been able to directly link changes in pyrite chemistry to the processes responsible for bonanza-style gold mineralization. Here, we report the results of high-resolution secondary-ion mass spectrometry and electron microprobe analyses conducted on pyrite from the Brucejack epithermal gold deposit, British Columbia. Our δ 34 S and trace element results reveal that the Brucejack hydrothermal system experienced abrupt fluctuations in fluid chemistry, which preceded and ultimately coincided with the onset of ultra-high-grade mineralization. We argue that these fluctuations, which include the occurrence of extraordinarily negative δ 34 S values (e.g., −36.1‰) in zones of auriferous, arsenian pyrite, followed by sharp increases of δ 34 S values in syn-electrum zones of nonarsenian pyrite, were caused by vigorous, fault valve-induced episodic boiling (flashing) and subsequent inundation of the hydrothermal system by seawater. We conclude that the influx of seawater was the essential step to forming bonanza-grade electrum mineralization by triggering, through the addition of cationic flocculants and cooling, the aggregation of colloidal gold suspensions. Moreover, our study demonstrates the efficacy of employing high-resolution, in situ analytical techniques to map out individual ore-forming events in a hydrothermal system.
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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".