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Record W2092339191 · doi:10.2113/gssajg.115.4.499

PETROGENESIS OF CU-NI SULPHIDE ORES FROM O'OKIEP AND KLIPRAND, NAMAQUALAND, SOUTH AFRICA: CONSTRAINTS FROM CHALCOPHILE METAL CONTENTS

2012· article· en· W2092339191 on OpenAlexaff
Wolfgang D. Maier, M. A. G. Andreoli, David I. Groves, Sarah‐Jane Barnes

Bibliographic record

VenueSouth African Journal of Geology · 2012
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsPetrogenesisCorporationGeologyArchaeologyLibrary scienceGeochemistryHistoryPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

The petrogenesis of sulphide ores in the O’okiep district has remained controversial. Based mainly on the concentration of chalcophile metals (PGE, Cu, Ni), it is proposed that the sulphides segregated from a basaltic magma generated during melting of sub-continental lithospheric mantle. Sulphide saturation of the magma was delayed due to relatively high fO2 until crustal contamination occurred during the advanced stages of differentiation. The immiscible sulphide melt was entrained and fractionated in dynamic magma conduits. Sulphides enriched in monosulphide solid solution (mss) component precipitated at depth in the Kliprand area of southern Namaqualand to form the Hondekloof deposits, whereas the O’okiep ores crystallised at shallower levels from highly fractionated residual sulphide liquids enriched in intermediate solid solution (iss). Sulphides of intermediate composition occur at Ezelsfontein. In the context of this model, the O’okiep intrusions could represent the proximal magmatic members of an IOCG suite of deposits, raising the prospect for additional IOCG deposits elsewhere in southern Africa. The model also predicts an enhanced potential at O’okiep for undiscovered Ni sulphide ores at depth.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.216
Teacher spread0.191 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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