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Record W2335431108 · doi:10.1071/aseg2006ab008

Comparative lithogeochemistry of komatiites in the Norseman-Wiluna and Abitibi Greenstone Belts, and implications for nickel sulfide targeting

2006· article· en· W2335431108 on OpenAlexaff
Stephen J. Barnes, C. Michael Lesher, Rebecca Sproule

Bibliographic record

VenueASEG Extended Abstracts · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsLaurentian University
Fundersnot available
KeywordsOlivineGeochemistryGeologyGreenstone beltMantle (geology)Nickel sulfideSulfideArcheanMantle plumeTectonicsLithosphereSeismologyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

This paper uses large sets of whole-rock geochemical data to make comparisons between the komatiitic rocks of Abitibi (AGB) and Norseman-Wiluna (NWGB) greenstone terranes.The NWGB komatiite suite has a much higher proportion of highly olivine-enriched cumulates than the AGB suite, as indicated by data-density distributions on plots of MgO vs FeO and MgO vs Cr, although average compositions of the komatiite magmas in the two belts were not significantly different. NWGB komatiites appear generally more contaminated, on the basis of various ratios of strongly to moderately incompatible low-mobility trace elements.Both factors which are likely contributors to the much higher Ni sulfide resource endowment of the NWGB. The combination of high degrees of contamination and presence of olivine adcumulates in the NWGB attests to the presence of exceptionally high-intensity, prolonged eruptions, capable of forming long-lived entrenched magma pathways, represented by highly olivine-enriched cumulates, and capable of melting substrates to form orebodies. This is in contrast with more episodic, lower volume eruptions in the AGB. The contrast is interpreted as the result of crustal structure and tectonic setting, rather then the size and intensity of mantle plume sources.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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