Re-Os chronology of critical minerals: applications to hydrothermal graphite and nickeline in 5-element ore veins
Bibliographic record
Abstract
Critical metals like graphite and nickel are becoming increasingly important in the context of the global transition to green energy, yet the geological processes responsible for their formation remain poorly constrained in many deposit types. This thesis applies rhenium-osmium (Re-Os) geochronology to two critical mineral systems—hydrothermal graphite and nickeline (NiAs) in 5-element veins—to evaluate their suitability as geochronometers and to improve genetic models of ore formation. The first case study targets the high-grade Amitsoq graphite deposit in South Greenland, which is hosted within the Paleoproterozoic Ketilidian Mobile Belt. Re-Os dating of two graphite samples yields an isochron age of ca. 1645-1677 Ma with initial 187Os/188Os ratios between 1.309-1.348, suggesting late- to post-tectonic mineralization associated with retrograde fault reactivation. These results support a growing body of evidence indicating a unique period of global-scale graphite formation during late-stage orogenic events associated with the assembly of the supercontinent Nuna in the Paleoproterozoic. The second case study explores the viability of nickeline as a Re-Os chronometer through analysis of samples from 5-element veins in the Northwest Territories, Canada. Despite its abundance in 5-element vein systems, nickeline Re concentrations are generally low (0.11-3.07 ppb) and yield model ages with high scatter, suggesting a high susceptibility to whole or partial Re-Os disturbances. Further, nickeline exhibits a tendency to contain micro-inclusions of phases with a high affinity for Re that dominate Re-Os systematics. These results overwhelmingly demonstrate that nickeline is not a reliable host for Re-Os dating. However, associated minerals such as gersdorffite yielded more geologically plausible model ages, dating 5-element vein mineralization at Eldorado mine, N.W.T., Canada to ca. 1712 and 1523 Ma, following two major intrusion episodes with spatial association to the 5-element mineralization. The older age of ca. 1712 Ma constrains uraninite mineralization to have occurred within 30 Ma of the later arsenide mineralization, suggesting a much closer link between the two distinct styles of mineralization than previously thought.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".