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Record W3155811745 · doi:10.1130/g48697.1

Eruption risks from covert silicic magma bodies

2021· article· en· W3155811745 on OpenAlexaff
Shane M. Rooyakkers, John Stix, Kim Berlo, Maurizio Petrelli, Freysteinn Sigmundsson

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsSilicicGeologyMagmaMagma chamberRhyoliteVolcanoExplosive eruptionBasaltGeochemistrySeismologyEarth scienceVolcanic rock

Abstract

fetched live from OpenAlex

Unintentional encounters with silicic magma at ~2–2.5 km depth have recently occurred during drilling at three volcanoes: Kilauea (Hawaii), Menengai (Kenya), and Krafla (Iceland). Geophysical surveys had failed to warn about shallow magma before each encounter, and subsequent surveys at Krafla have been unable to resolve the size or architecture of its silicic magma body. This presents a conundrum for volcano monitoring: Do such shallow “covert” magma bodies pose an eruption risk? Here, we show that Krafla’s most recent explosive eruption, a mixed hydrothermal-magmatic event in 1724 C.E. that formed the Víti maar, involved rhyolite essentially indistinguishable in composition from magma encountered during drilling in 2009. Streaks of quenched basalt in some Víti pumices provide direct evidence for interaction between co-erupted rhyolitic and basaltic magmas, but crystals in these pumices show no evidence for late-stage heating or re-equilibration with more mafic melt, implying mixing time scales of at most several hours. Covert silicic magma thus presents an eruption risk at Krafla and may be mobilized with little warning. Difficulties in resolving magma bodies smaller than ~1 km3 with geophysical surveys mean that covert silicic magma may exist at many other volcanoes and should be considered in hazard and risk assessments.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.998

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)0.0880.003

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2021
Admission routes1
Has abstractyes

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