Using Rare Earth Elements (REE) to Decipher the Origin of Ore Fluids Associated with Granite Intrusions
Bibliographic record
Abstract
A practical method is presented to estimate rare earth element (REE) concentrations in a magmatic fluid phase in equilibrium with water-saturated granitic melts based on empirical fluid–melt partition coefficients of REE ( k P R E E ). The values of k P R E E can be calculated from a set of new polynomial equations linking to the Cl molality ( m C l v ) of the magmatic fluid phase associated with granitic melts, which are established via a statistical analysis of the existing experimental dataset. These equations may be applied to the entire pressure range (0.1 to 10.0 kb) within the continental crust. Also, the results indicate that light REEs (LREE) behave differently in magmatic fluids, i.e., either being fluid compatible with higher m C l v or fluid incompatible with lower m C l v values. In contrast, heavy REEs (HREE) are exclusively fluid incompatible, and partition favorably into granitic melts. Consequently, magmatic fluids tend to be rich in LREE relative to HREE, leading to REE fractionation during the evolution of magmatic hydrothermal systems. The maximum k P R E E value for each element is predicted and presented in a REE distribution diagram constrained by the threshold value of m C l v . The REE contents of the granitic melt are approximated by whole-rock analysis, so that REE concentrations in the associated magmatic fluid phase would be estimated from the value of k P R E E given chemical equilibrium. Two examples are provided, which show the use of this method as a REE tracer to fingerprint the source of ore fluids responsible for the Lake George intrusion-related Au–Sb deposit in New Brunswick (Canada), and the Bakircay Cu–Au (–Mo) porphyry systems in northern Turkey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.017 | 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 teacher head, 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".