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Record W1854257507 · doi:10.1063/1.4930095

Mechanical and thermal properties of γ-Mg2SiO4 under high temperature and high pressure conditions such as in mantle: A first principles study

2015· article· en· W1854257507 on OpenAlexaff
Jianwei Xiao, Roderick Melnik, Yoshiyuki Kawazoe, Bin Wen

Bibliographic record

VenueThe Journal of Chemical Physics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsWilfrid Laurier University
FundersNational Natural Science Foundation of China
KeywordsThermal conductivityThermodynamicsThermal expansionThermalGrüneisen parameterMaterials scienceHigh pressureMantle (geology)PhononWork (physics)MineralogyGeologyCondensed matter physicsGeophysicsPhysics

Abstract

fetched live from OpenAlex

γ-Mg2SiO4 is an important mineral in mantle, and our knowledge on its mechanical and thermal properties is critical for many areas of geological sciences. In this work, the crystal structure of γ-Mg2SiO4 under high temperature and high pressure conditions is optimized by using the GOMASC method, and the total energy, thermal expansion coefficients, and elastic constants at different temperature and pressure conditions are obtained. On the basis of phonon spectrum, group velocity, phase velocity, Grüneisen parameter, and thermal conductivity are calculated for γ-Mg2SiO4 under high temperature and high pressure conditions. These calculated results can provide an important reference for geological research.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
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 head0.024
GPT teacher head0.219
Teacher spread0.194 · 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 designSimulation or modeling
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

Citations17
Published2015
Admission routes1
Has abstractyes

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