Mass transfer during gold precipitation within a vertically extensive vein network (Sigma deposit - Abitibi greenstone belt - Canada). Part II. Mass transfer calculations
Bibliographic record
Abstract
A companion work (Garofalo, this issue) shows that along the Archean, Au-mineralized quartz-tourmaline veins of the Sigma deposit of Canada, fluid-rock interaction generated two types of hydrothermal alteration during gold deposition, one tourmaline-rich and another albite-rich. This work combines petrographic and mass transfer studies to monitor the chemical exchange occurring between the Au-bearing hydrothermal fluid within the veins and a porphyritic diorite wall rock in which two alteration envelopes of the different types developed. Tourmaline-rich alteration consists of replacement of the host rock by tourmaline-calcite-quartz-pyrite-pyrrhotite at the vein walls. In these haloes, components like SiO2, B, Al2O3, Na2O, CO2, CaO, S, andAu were transferred from the vein fluid to the host rock, while other components like K2O and FeO were transferred from the rock to the fluid. Albite-rich alteration consists of progressive replacement of biotite, chlorite, epidote, and quartz of the host rock by the assemblage albite-pyrite-pyrrhotite, and the trends of gains and losses are opposite of those calculated for the tourmaline-rich haloes. Only B, S, and Au are transferred from the fluid to the rock in both alteration types. Progressive mineralogical and chemical changes of thewall rock during alteration indicate that diffusion metasomatism was important for generating the two alteration types. These results underscore some key characteristics of the hydrothermal alteration at Sigma. First, the distinct alteration types formed most probably from chemically distinct hydrothermal fluids, in agreement with independent fluid inclusion data (Garofalo et al., 2002b). These two fluids reacted with the wall rocks, producing the contrasting mineralogical and chemical modifications recorded within the albite-rich and tourmaline-rich haloes. The trends of mass transfers are peculiar because the components given by the albite-rich haloes to the vein fluid are those given by the vein fluid to the tourmaline-rich haloes. Hence, albite-rich alteration was functional to the generation of tourmaline-rich haloes in the wall rocks and to tourmaline precipitation with the veins. Second, the combined transfers into and out of the veins caused modifications in the concentration ofmajor components in the fluid, like SiO2 and Al2O3. The concentration ratios of other major fluid components like Na and K remained unchanged because of buffering from the vein mineral assemblage, while that of some minor and trace components like REE, Rb, Sr, and high field strength elements varied significantly. A comparison between the mass transfer data presented here and that of other vein-hosted Au deposits with alteration characteristics similar to Sigma shows that, with the exception of Au, S, and CO2, inconsistent mass transfer trends are typical in these deposits and include the so-called “immobile” components (i.e. Al2O3, TiO2, and Zr). This shows that mass transfer data cannot be used for formulating genetic models of vein hosted Au deposits at the global scale, but mainly for constraining models for single deposits, where the background geological and geochemical information are in general well defined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".