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Record W3162832540 · doi:10.1130/g48715.1

The interfacial energy penalty to crystal growth close to equilibrium

2021· article· en· W3162832540 on OpenAlexaff
Fred Gaidies, Freya George

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeologyMicaMatrix (chemical analysis)Crystal (programming language)Growth rateCrystal growthSurface energyMineralogyMaterials scienceCrystallographyGeometryComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract Understanding the origin of rock microstructure is critical for refining models of the geodynamics of the Earth. We use the geometry of compositional growth zoning of a population of garnet porphyroblasts in a mica schist to gain quantitative insight into (1) the relative growth rates of individual crystals, (2) the departure from equilibrium during their growth, and (3) the mobility of the porphyroblast-matrix interface. The driving force for garnet growth in the studied sample was exceedingly small and is comparable in magnitude to the interfacial energy associated with the garnet-matrix interface. This resulted in size-dependent garnet growth at macroscopic length scales, with a decrease in radial growth rates for smaller crystals caused by the penalty effect of the interfacial energy. The difference in growth rate between the largest and the smallest crystal is ~45%, and the interface mobility for garnet growth from ~535°C, 480 MPa to 565°C, 560 MPa in the phyllosilicate-dominated rock matrix ranged between ~10−19 and 10−20 m4 J−1 s−1. This is the first estimation of interface mobility in natural rock samples. In addition to the complex structural and chemical reorganization associated with the formation of dodecahedral coordination polyhedra in garnet, the presence of abundant graphite may have exerted drag on the garnet-matrix interface, further decreasing its mobility.

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 score0.383
Threshold uncertainty score1.000

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)0.0120.001

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

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