Platinum-group element and gold contents of arsenide and sulfarsenide minerals associated with Ni and Au deposits in Archean greenstone belts
Bibliographic record
Abstract
ABSTRACT Post-magmatic alteration of certain magmatic Ni sulfide ores in Western Australia, the Miitel deposit and the Sarah's Find prospect, produced Ni–As–PGE haloes around massive sulfides. A study of the composition of arsenide grains from these hydrothermal haloes, along with arsenides from various magmatic and hydrothermal mineralized environments in other localities, was conducted in order to compare their composition, and assess their potential use as indicator minerals for exploration vectoring, as well as to gain knowledge on their crystallization history. Concentrations in trace elements such as platinum-group elements (PGEs), Au and other metals was obtained by laser ablation inductively coupled plasma mass spectroscopy analyses. Results show that variations in PGEs and Au compositions can be related to the magmaticvs.hydrothermal origin of the grains; and to their provenance from deposits enriched in either Ni, Au or both. Magmatic NiCoFe sulfarsenides have strongly correlated, high IPGE (Os, Ir, Ru, Rh) contents up to 100 ppm Ir, compared with maximum values in hydrothermal sulfarsenides of ~1 ppm. Gold in hydrothermal sulfarsenides from Au-mineralized ultramafic rocks extends up to 500 ppm, with typical values of 3–30 ppm; similar values are also found in nickeline (also called niccolite). These results suggest that nickel arsenides could potentially be used as indicator minerals for nickel and gold exploration. Trace-element contents of arsenide grains in shear zones could be used to deduce the presence of Ni or Au mineralization upstream in the fluid pathway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".