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Record W4310472942 · doi:10.3390/min12121529

Occurrences of Niobium and Tantalum Mineralization in Mongolia

2022· article· en· W4310472942 on OpenAlexafffund
J. Dostál, Ochir Gerel

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeralkaline rockGeochemistryFelsicMineralization (soil science)PegmatiteColumbiteGeologyTantalumMineralogyChemistryMafic

Abstract

fetched live from OpenAlex

Niobium and tantalum are two rare metals that have similar physical and chemical properties and occur together in nature. They are considered to be strategic and critical materials for the economy and national security of many industrial countries. Both elements are on the 2022 List of Critical Minerals of the USA as well as on the European Union’s List of Critical Raw Materials. They rarely substitute for common elements in rock-forming minerals but are essential components in a range of rare minerals, particularly oxides and subordinately silicates. The economically important minerals are oxides. The columbite-tantalite and pyrochlore-microlite groups are the most common Ta- and Nb-bearing minerals. In Mongolia, primary niobium and tantalum mineralization includes two main types. The first type is mineralization associated with alkaline to peralkaline granites, pegmatites and syenites whereas the second type is related to the lithium-fluorine-rich peraluminous granites and related rocks (pegmatites and ongonites). The host rocks of both types of mineralization are the fractionated felsic rocks, which contain the primary magmatic ore assemblages associated with fractionation of magma rich in rare metals. Both assemblages were subsequently overprinted by the late magmatic to hydrothermal fluids, which remobilized and enriched the original mineralization. The newly formed ore mineral assemblages display complex replacement textures. In the case of peralkaline felsic rocks the processes produced the mineralization of Zr, Nb, heavy REE, Y, U, Th and Ta whereas peraluminous Li-F felsic rocks contain mainly mineralization of Sn, W, Ta, Li, and Nb. Mongolia hosts several promising occurrences of both types of Nb-Ta mineralization. However, they have not yet been sufficiently explored. Currently, the most promising is the occurrence in the Devonian Khalzan Buregtei peralkaline granites in northwestern Mongolia, where Nb-Ta is associated with REE and Zr mineralization. Mesozoic carbonatites of southern Mongolia do not host significant Nb and Ta mineralization.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.997

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)0.0100.000

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 head0.012
GPT teacher head0.196
Teacher spread0.184 · 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 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

Citations14
Published2022
Admission routes2
Has abstractyes

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