Application of Ni, Cu and Fe isotopes as indicators of ore genesis - New insights from the epigenetic-hydrothermal Rajapalot Au Co prospect, Finnish Lapland
Bibliographic record
Abstract
Application of stable transition metal isotopes as indicators of ore genesis is becoming more popular, yet the fractionation mechanisms and isotopic distribution in these unconventional systems remain poorly understood. In this study, we present an analysis of sulphide Ni, Cu and Fe isotopes measured from solution using multicollector ICP-MS. The data were collected from the dominant sulphide phases in the Raja prospect within the epigenetic-hydrothermal Rajapalot Au Co deposit, Finnish Lapland. Our main goal was to gain new information on the systematics and behaviour of the isotopes in high-temperature ore-forming environments, with implications for ore genesis. The Raja prospect is hosted by a Paleoproterozoic volcanic-sedimentary sequence and was formed by multi-stage hydrothermal processes during the Svecofennian orogeny. Pyrite shows significant variation in δ 56 Fe (-2.08 to +3.29 ‰), including the heaviest iron isotopes thus far observed in natural pyrite. The δ 56 Fe values in pyrrhotite vary less (-0.74 to +0.80 ‰) but are unusually heavy compared to those of magmatic pyrrhotite. δ 56 Fe in chalcopyrite ranges from +0.10 to +1.45 ‰, δ 60 Ni in pyrrhotite from -1.03 to +0.18 ‰, and δ 65 Cu in chalcopyrite from -0.30 to +0.23 ‰. The δ 56 Fe values in co-existing sulphide phases suggest both equilibrium and kinetic fractionation effects. The extreme Fe fractionation in pyrite implies that kinetic fractionation played a major role in the precipitation of isotopically light pyrite. Moreover, inheritance of low δ 56 Fe values from a pyrrhotite precursor is likely. The heavy Fe isotopic composition of some of the pyrrhotite and pyrite is probably the result of preferential leaching of light isotopes by late hydrothermal fluids. Systematic correlations between the composition of the examined isotope systems, Co and Au concentrations, and textural features link the sulphide isotopes to multi-stage ore formation. Gradual trends in the isotope compositions suggest Rayleigh fractionation. Early Co deposition is attributed to isotopically heavy fluid, probably derived from a sedimentary formation with abundant iron oxides. The isotopically lighter Au mineralising fluids point to a separate fluid source, probably involving evolved granites. The late hydrothermal Au-carrying fluids overprinted the early Co mineralisation forming Au Co enriched zones. Our study highlights the potential of multiple isotope systematics in sulphides as a useful diagnostic tool for tracing mineralisation processes in and source regions of hydrothermal Au and Co. • Application of stable Ni, Cu, and Fe isotopes as a new tool to study ore genesis. • Isotopic fractionation in a high-temperature ore-forming environment. • Accurate isotope analysis from sulphides. • Significant insights into the isotopic behaviour in hydrothermal systems. • Potential applications in tracing ore genesis.
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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 | 0.000 |
| 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) | 0.000 | 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".