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Record W4281744544 · doi:10.1139/cjp-2021-0365

Tilt grain boundary energy in 〈0001〉/<i>φ</i> bicrystals of pure ice

2022· article· en· W4281744544 on OpenAlexvenueno aff
C.L. Di Prinzio, D. Stoler, E. Druetta

Bibliographic record

VenueCanadian Journal of Physics · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMisorientationPhysicsGrain boundaryCondensed matter physicsTilt (camera)CrystallographyLattice (music)Crystal twinningFacet (psychology)PolarGeometryMicrostructureChemistry

Abstract

fetched live from OpenAlex

This work presents measurements of the relative energy ( γ gb / γ s ) of the tilt grain boundary (GB) in pure ice bicrystals with crystalline misorientation 〈0001〉/ φ. The GBs had different inclinations and were annealed at −5 °C and −18 °C. A GB structure model called the facet model (FM) was applied. The FM assumes that the GB is composed of facets. The facets are oriented with the principal faces of the cell primitive of the coincident site lattice (CSL). The CSL is associated with the bicrystalline misorientation. In general, ( γ gb / γ s ) varies by more than one order of magnitude between temperatures −5 °C ( γ gb / γ s ~ 0.03) and −18 °C ( γ gb / γ s ~ 0.35), so GB structural changes are very noticeable with temperature. The GB energy does not depend on the inclination at temperatures near 0 °C, while the dependence on inclination presents a slight significance at −18 °C. Finally, it was observed that the GB relative energy ( γ gb / γ s ) for bicrystalline 〈0001〉/ φ samples is lower than the relative energy at other crystalline misorientations. These experimental data are unique and could explain the slow GB migration in 〈0001〉/ φ bicrystals and they could help to understand grain growth in polar ice where the most frequent crystalline misorientation is 〈0001〉/ φ.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
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)0.0030.000

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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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.

Study designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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