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Record W2810091991 · doi:10.1029/2018gl077799

Shear Rate‐Dependent Disequilibrium Rheology and Dynamics of Basalt Solidification

2018· article· en· W2810091991 on OpenAlexaff
S. Kolzenburg, Daniele Giordano, Kai‐Uwe Hess, Donald B. Dingwell

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcGill University
FundersHorizon 2020 Framework ProgrammeH2020 Marie Skłodowska-Curie ActionsCompagnia di San PaoloH2020 European Research CouncilFondazione CRT
KeywordsRheologyLavaBasaltGeologyOlivineShear (geology)Shear ratePetrologyShear zoneMagmaPeridotiteCrystallizationMineralogyGeochemistryGeophysicsThermodynamicsVolcanoMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Magmas and lavas undergo a range of shear rates during transport and emplacement. Further, transport of magma and lava occurs at subliquidus conditions where the melt crystallizes at varying temperature, pressure, and oxygen fugacity. Transport efficiency and eruption style are governed by magma rheology, which evolves during cooling, crystallization and degassing. Quantification of magma rheology rests almost exclusively on experimentation at constant temperature and shear rate. We present the first study on the effect of shear rate on subliquidus basalt rheology at conditions relevant to lava flows and shallow magmatic systems. The results reveal that basalts reach their rheologic death or cutoff temperature (Tcutoff; i.e., the point at which the sample rheologically solidifies and flow stops) at higher temperatures when flowing faster, whereas crystallization is suppressed when the shear rate is low. We explore the implications of shear‐enhanced crystallization for modeling and forecasting of lava flow hazards and our understanding of magma and lava transport/storage systems.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.027
GPT teacher head0.275
Teacher spread0.247 · 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 designBench or experimental
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

Citations51
Published2018
Admission routes1
Has abstractyes

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