The Neglected Involvement of Organic Matter in Forming Large and Rich Hydrothermal Orogenic Gold Deposits
Bibliographic record
Abstract
Orogenic gold deposits have provided most of gold to humanity. These deposits were formed by fluids carrying dissolved gold at temperatures of 200–500 °C and at crustal depths of 4–12 km. The model involves gold mobilization as HS− complexes in aqueous solution buffered by CO2, with gold precipitation following changes in pH, redox activity (fO2), or H2S activity. In this contribution, the involvement of carbonaceous organic matter is addressed by considering the formation of large and/or rich orogenic gold deposits in three stages: the source of gold, its solubilization, and its precipitation. First, gold accumulates in nodular pyrite within carbonaceous-rich sedimentary rocks formed by bacterial reduction of sulfates in seawater in black shales. Second, gold can be transported as hydrocarbon-metal complexes and colloidal gold nanoparticles for which the hydrocarbons can be generated from the thermal maturation of gold-bearing black shales or from abiotic origin. The capacity of hydrocarbons for solubilizing gold is greater than those of aqueous fluids. Third, gold can be precipitated efficiently with graphite derived from fluids containing hydrocarbons or by reducing organic-rich rocks. Black shales are thus a key component in the formation of large and rich orogenic gold deposits from the standpoints of source, transport, and precipitation. Unusual CO2-rich, H2O-poor fluids are documented for some of the largest and richest orogenic gold deposits, regardless of their age. These fluids are interpreted to result from chemical reactions involving hydrocarbon degradation, hence supporting the fundamental role of organic matter in forming exceptional orogenic gold 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".