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Record W180451813 · doi:10.3133/sir20105070i

Occurrence model for magmatic sulfide-rich nickel-copper-(platinum-group element) deposits related to mafic and ultramafic dike-sill complexes

2014· article· en· W180451813 on OpenAlexaboutno aff
Klaus J. Schulz, Laurel G. Woodruff, Suzanne W. Nicholson, Robert R. Seal, Nadine M. Piatak, V.W. Chandler, John L. Mars

Bibliographic record

VenueScientific investigations report · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsUltramafic rockMaficGeologyGeochemistrySulfidePlatinum groupFlood basaltArcheanNickel sulfideBasaltVolcanogenic massive sulfide ore depositPyritePlatinumChemistryVolcanism

Abstract

fetched live from OpenAlex

Magmatic sulfide deposits containing nickel (Ni) and copper (Cu), with or without (±) platinum-group elements (PGE), account for approximately 60 percent of the world’s nickel production. Most of the remainder of the Ni production is derived from lateritic deposits, which form by weathering of ultramafic rocks in humid tropical conditions. Magmatic Ni-Cu±PGE sulfide deposits are spatially and genetically related to bodies of mafic and/or ultramafic rocks. The sulfide deposits form when the mantle-derived mafic and/or ultramafic magmas become sulfide-saturated and segregate immiscible sulfide liquid, commonly following interaction with continental crustal rocks. Deposits of magmatic Ni-Cu sulfides occur with mafic and/or ultramafic bodies emplaced in diverse geologic settings. They range in age from Archean to Tertiary, but the largest number of deposits are Archean and Paleoproterozoic. Although deposits occur on most continents, ore deposits (deposits of sufficient size and grade to be economic to mine) are relatively rare; major deposits are present in Russia, China, Australia, Canada, and southern Africa. Nickel-Cu sulfide ore deposits can occur as single or multiple sulfide lenses within mafic and/or ultramafic bodies with clusters of such deposits comprising a district or mining camp. Typically, deposits contain ore grades of between 0.5 and 3 percent Ni and between 0.2 and 2 percent Cu. Tonnages of individual deposits range from a few tens of thousands to tens of millions of metric tons (Mt) bulk ore. Two giant Ni-Cu districts, with ≥10 Mt Ni, dominate world Ni sulfide resources and production. These are the Sudbury district, Ontario, Canada, where sulfide ore deposits are at the lower margins of a meteorite impact-generated igneous complex and contain 19.8 Mt Ni; and the Noril’sk-Talnakh district, Siberia, Russia, where the ore deposits are in subvolcanic mafic intrusions related to flood basalts and contain 23.1 Mt Ni. In the United States, the Duluth Complex in Minnesota, comprised of a group of mafic intrusions related to the 1.1 Ga Midcontinent Rift system, represents a major Ni resource of 8 Mt Ni, but deposits generally exhibit low grades (0.2 percent Ni, 0.66 percent Cu) and remain in the process of being proven economic. The sulfides in magmatic Ni-Cu deposits generally constitute a small volume of the host rock(s) and tend to be concentrated in the lower parts of the mafic and/or ultramafic bodies, often in physical depressions or areas marking changes in the geometry of the footwall topography. In most deposits, the sulfide mineralization can be divided into disseminated, matrix or net, and massive sulfide, depending on a combination of the sulfide content of the rock and the silicate texture. The major Ni-Cu sulfide mineralogy typically consists of an intergrowth of pyrrhotite (Fe7S8), pentlandite ([Fe, Ni]9S8), and chalcopyrite (FeCuS2). Cobalt, PGE, and gold (Au) are extracted from most magmatic Ni-Cu ores as byproducts, although such elements can have a significant impact on the economics in some deposits, such as the Noril’sk-Talnakh deposits, which produce much of the world’s palladium. In addition, deposits may contain between 1 and 15 percent magnetite associated with the sulfides.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.234
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2014
Admission routes1
Has abstractyes

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