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Record W4392159402 · doi:10.1017/jog.2024.8

Formation and persistence of glaciovolcanic voids explored with analytical and numerical models

2024· article· en· W4392159402 on OpenAlexafffundabout
Tryggvi Unnsteinsson, Gwenn E. Flowers, Glyn Williams‐Jones

Bibliographic record

VenueJournal of Glaciology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsGeothermal gradientGeologyGlacierVolcanoSnowVoid (composites)Heat fluxGeomorphologyScalingGeophysicsPetrologyMechanicsGeochemistryGeometryHeat transferMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract One fifth of Earth's volcanoes are covered by snow or ice and many have active geothermal systems that interact with the overlying ice. These glaciovolcanic interactions can melt voids into glaciers, and are subject to controls exerted by ice dynamics and geothermal heat output. Glaciovolcanic voids have been observed to form prior to volcanic eruptions, which raised concerns when such features were discovered within Job Glacier on Qw̓elqw̓elústen (Mount Meager Volcanic Complex), British Columbia, Canada. In this study we model the formation, evolution, and steady-state morphology of glaciovolcanic voids using analytical and numerical models. Analytical steady-state void geometries show cave height limited to one quarter of the ice thickness, while numerical model results suggest the void height h scales with ice thickness H and geothermal heat flux $\dot {Q}$ as $h/H = a H^b \dot {Q}^c$ , with exponents b = − n /2 and c = 1/2 where n is the creep exponent. Applying this scaling to the glaciovolcanic voids within Job Glacier suggests the potential for total geothermal heat flux in excess of 10 MW. Our results show that relative changes in ice thickness are more influential in glaciovolcanic void formation and evolution than relative changes in geothermal heat flux.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.135

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)0.0000.000

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 head0.043
GPT teacher head0.228
Teacher spread0.185 · 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.

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

Citations5
Published2024
Admission routes3
Has abstractyes

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