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Record W4306391228 · doi:10.1038/s41467-022-33302-z

Genetic model of the El Laco magnetite-apatite deposits by extrusion of iron-rich melt

2022· article· en· W4306391228 on OpenAlexaff
Tobias Keller, Fernando Tornos, John M. Hanchar, Dorota K. Pietruszka, Arianna Soldati, Donald B. Dingwell, Jenny Suckale

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMagnetiteApatiteExtrusionMaterials scienceMetallurgyChemical engineeringGeochemistryMineralogyGeologyEngineering

Abstract

fetched live from OpenAlex

Magnetite-apatite deposits are important sources of iron and other metals. A prominent example are the magnetite lavas at the El Laco volcano, Northern Chile. Their formation processes remain debated. Here, we test the genetic hypothesis that an Fe-rich melt separated from silicate magma and ascended along collapse-related fractures. We complement recent analyses with thermodynamic modelling to corroborate Fe-Si liquid immiscibility evident in melt inclusions at El Laco and present viscometry of Fe- and Si-rich melts to assess the time and length scales of immiscible liquid separation. Using a rock deformation model, we demonstrate that volcano collapse can form failure zones extending towards the edifice flanks along which the ore liquid ascends towards extrusion driven by vapour exsolution despite its high density. Our results support the proposed magmatic genesis for the El Laco deposits. Geochemical and textural similarities indicate magnetite-apatite deposits elsewhere form by similar processes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations22
Published2022
Admission routes1
Has abstractyes

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