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Molten Salts: Fluid Inclusion Record and Role in Forming Mineral Deposits

2025· article· en· W4415975513 on OpenAlexaff
Matthew Steele‐MacInnis

Bibliographic record

VenueAnnual Review of Earth and Planetary Sciences · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluid inclusionsInclusion (mineral)CarbonateMineralCarbonate minerals

Abstract

fetched live from OpenAlex

Fluid inclusions provide a wealth of information on the compositions, temperatures, and other properties of fluids that form mineral deposits. Fluid inclusions representative of aqueous-hydrothermal ore-forming fluids have been studied extensively over many years, and our understanding of their properties is comprehensive. But in recent years, evidence has been mounting for widespread occurrence of novel and unexpected types of fluid inclusions. These fluids are composed of molten alkali-calcic chloride, sulfate, and carbonate components, and they contain little to no H 2 O. Their physical and chemical properties are only starting to be explored, but evidence for their capacity to mobilize certain metals, and their participation in crustal ore-forming processes, is growing. The objectives of this review are to showcase these novel solutions and to discuss their origins and roles in forming mineral deposits. ▪ Fluid inclusions in minerals reveal widespread occurrence of natural molten salts. ▪ Evidence for molten salts, composed of chloride, sulfate, and carbonate components, is reported in numerous ore deposits. ▪ Molten salts are low-viscosity fluids, highly chemically reactive, and capable of transporting high concentrations of critical metals. ▪ Molten salts represent a novel and unexpected type of crustal ore-forming fluid.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.247
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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