From micro to mega: fluid inclusion data sets influence large-scale genetic models of orogenic gold deposits
Bibliographic record
Abstract
More than three decades of fluid inclusion studies of orogenic deposits have provided geologists with the knowledge of the fundamental physical-chemical properties of Au-depositing hydrothermal fluids within the Earth’s crust. To date, it is common knowledge that these ore fluids are aquo-carbonic in nature (CO2 = 5-15 mol%), with salinities that typically do not exceed 10 wt% NaCleq and Thtot in the 300-400 °C range. It is also common knowledge that these properties are documented with surprising consistency in Archean terranes of Canada, Africa, Australia, and India, and are not much different from those of much younger orogenic belts (e.g., Lachlan fold belt of Central Victoria, Australia). This consistent occurrence of similar fluid properties in distinct orogenic belts has represented an important argument for a unique genetic process of orogenic deposits, valid across time and space. This model would consider a stage of fluid production at the root zones of orogenic belts (source region), and a stage of fluid focusing and ore genesis typically within quartz-filled, brittle-ductile faults (veins) at shallow structural levels. \nBeing conceptually simple and appealing, this model has generated a lot of consensus in the scientific community; however, it also generated a vigorous debate on the actual identity of the source fluid, as virtually all the imaginable genetic processes at the source region were proposed in the last years, i.e. mantle vs. magmatic vs. heating of a deeply-driven meteoric fluid. This spectrum of models is based on stable isotope data, petrologic and timing arguments, and field associations, and keeps the fluid inclusion data at the background of data interpretation and discussion. In this talk, I will show how the fluid inclusion data collected during the last decades of research have progressively lost their initial influence in the construction of genetic models of orogenic deposits. A detailed survey of these data shows that past studies (1) do not identify unequivocal petrographic relationships between the entrapment of ore fluid within vein minerals and Au precipitation, (2) are based on poor quantitative constraints of the phase proportions in the fluid (i.e., the liquid/vapour ratio), and (3) do provide a quantitative estimation of the concentration of Au and other major and trace fluid components (i.e., Na, K, Ca, B, W, As, Sb, and Ag) in the ore fluid. Thus, the previous database should be re-considered and amended where needed. \nThe data I will present show how the systematic application of an up-to-date set of fluid inclusion analytical techniques in a limited number of well-known Au deposits of different categories (i.e., not only orogenic in the classical sense) amends and completes the previous knowledge on Au-transporting hydrothermal fluids, providing a more effective background for comprehensive genetic models of Au 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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".