MétaCan
Menu
Back to cohort
Record W4210604722 · doi:10.2138/am-2022-8327

Nickel in olivine as an exploration indicator for magmatic Ni-Cu sulfide deposits: A data review and re-evaluation

2022· review· en· W4210604722 on OpenAlexaff
Stephen J. Barnes, Zhuosen Yao, Ya‐Jing Mao, Ana Patrícia Jesus, Sheng-Hong Yang, Valentina Taranovic, Wolfgang D. Maier

Bibliographic record

VenueAmerican Mineralogist · 2022
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsOlivineGeologySulfideGeochemistryFractional crystallization (geology)MaficUltramafic rockIgneous differentiationMelt inclusionsMantle (geology)Layered intrusionMineralogyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract Nickel contents of olivine have been widely used as petrogenetic indicators and as fertility indicators for magmatic sulfide potential of mafic-ultramafic intrusions, on the assumption that olivines crystallized from magmas that had equilibrated with sulfide liquid should be relatively depleted in Ni compared with a sulfide-free baseline. This has given rise to a large accumulation of data that is brought together here, along with data on volcanic olivines, to critically evaluate the effectiveness of the approach. We identify multiple sources of variance in Ni content of olivine at a given Fo content, including variability in mantle melt composition due to depth, water content (and possibly source), subsequent fractional crystallization with and without sulfide, recharge and magma mixing, batch equilibration between olivine and sulfide at variable silicate-sulfide ratio (R), and olivine/liquid ratio; and subsequent equilibration during trapped liquid crystallization in orthocumulates. Baselines for Ni in olivine in relation to Fo content are somewhat lower in orogenic belt settings relative to intrusions in continental large igneous provinces (LIPs). This is probably related to differences in initial parent magma compositions, with plume magmas generally forming deeper and at higher temperatures. No clear, universal discrimination is evident in Ni in olivine between ore-bearing, weakly mineralized, and barren intrusions, even when tectonic setting is taken into account. However, sulfide-related signals can be identified at the intrusion scale in many cases. Low-R factor and low-tenor sulfides are associated with low-Ni olivines in several examples, and these cases stand out clearly. Anomalously high-Ni olivines are a feature of some mineralized intrusions, in part due to trapped liquid reaction effects. However, in some cases, this mechanism cannot account for the magnitude of enrichment. In these cases, enrichment may be due to re-entrainment of “primitive” Ni-rich sulfide by a more evolved Fe-rich magma, driving the olivine to become Ni-enriched due to Fe-Ni exchange reaction between sulfide and olivine during transport. An extreme case of this process may account for ultra-Ni enriched olivine at Kevitsa (Finland), but more subtle signals elsewhere could be positive indicators. A lack of clear mineralized/barren distinction in specific groups of related intrusions, e.g., the deposits of NW China or the Kotalahti Belt in Finland, may well be due to “false negatives” where undiscovered mineralization exists in specific intrusions or in their feeder systems, or may also be due to a multiplicity of confounding factors. Wide variability of both Fo and Ni between related intrusions at regional scale may be a useful regional prospectivity indicator, more than an intrusion-scale discriminant, and is certainly informative as a petrogenetic indicator. In general, the use of Ni-olivine as a fertility tool is more likely to generate false negatives than false positives, but both are possible, and the technique should be used as part of a broader weight-of-evidence approach.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.142
GPT teacher head0.357
Teacher spread0.216 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations39
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAmerican MineralogistSame topicGeological and Geochemical AnalysisFrench-language works237,207