AN OVERVIEW OF CHALCOPHILE ELEMENT CONTENTS OF PYRRHOTITE, PENTLANDITE, CHALCOPYRITE AND PYRITE FROM MAGMATIC NI-CU- PGE DEPOSITS
Bibliographic record
Abstract
We have compiled the trace element concentrations in pyrrhotite, pentlandite, chalcopyrite, and pyrite from magmatic Ni-Cu-PGE ore deposits with the aim of understanding their petrogenesis and whether these minerals can be used as indicator minerals. Among the samples, there are some of the most studied world-class Ni-Cu- (Aguablanca, Duluth, Jinchuan, Noril’sk-Talnakh-Kharaelakh, Sudbury, Voisey’s Bay, and others) and PGE-dominated (Bushveld, Lac des Iles, Stillwater, Great Dyke, and Penikat) deposits. Crustal assimilation may be constrained using As/Se and Sb/Se ratios in pentlandite. The degree of interaction between the silicate and sulfide liquids (R-factor) can be estimated by the content of highly chalcophile elements (Dsulf liq/sil liq above 1000) in sulfide minerals. The fractional crystallization of the sulfide liquid can be traced using Se/Te ratios of pentlandite. Pyrite formed by exsolution from MSS has higher Rh, Ru, Ir, and Os than co-existing pyrrhotite, whereas pyrite formed by hydrothermal alteration of pyrrhotite inherits the Rh, Ru, Ir, and Os contents of the pyrrhotite it replaced. Sulfide minerals are preserved in transported glacial cover and their trace element chemistry can be used to discriminate their source. Pentlandite from Ni-Cu deposits has much lower Rh and Pd concentrations than those from PGE-dominated deposits, pyrite from magmatic deposits has higher Co/Sb and Se/As ratios relative to pyrite from hydrothermal deposits, and chalcopyrite from magmatic deposits has much higher Ni and lower Cd concentrations than those from hydrothermal deposits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| 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".