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Record W2397115253 · doi:10.7185/geochemlet.1617

CO2-fluxing collapses metal mobility in magmatic vapour

2016· article· en· W2397115253 on OpenAlexafffund
Vincent van Hinsberg, Kim Berlo, Artas Migdisov, Anthony E. Williams‐Jones

Bibliographic record

VenueGeochemical Perspectives Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsGeologyMetallurgyMaterials scienceEnvironmental science

Abstract

fetched live from OpenAlex

Magmatic systems host many types of ore deposits, including world-class deposits of copper and gold.Magmas are commonly an important source of metals and ore-forming fluids in these systems.In many magmatic-hydrothermal systems, low-density aqueous fluids, or vapours, are significant metal carriers.Such vapours are water-dominated shallowly, but fluxing of CO 2 -rich vapour exsolved from deeper magma is now recognised as ubiquitous during open-system magma degassing.Here, we show that such CO 2 -fluxing leads to a sharp drop in element solubility, up to a factor of 10,000 for Cu, and thereby provides a highly efficient, but as yet unrecognised mechanism for metal deposition.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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
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

Citations26
Published2016
Admission routes2
Has abstractyes

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