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A rheological model for glassforming silicate melts in the systems CAS, MAS, MCAS

2007· article· en· W2016185677 on OpenAlexaff
Daniele Giordano, James K. Russell

Bibliographic record

VenueJournal of Physics Condensed Matter · 2007
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsViscosityRheologyThermodynamicsSilicateSlippingComposition (language)Materials scienceGibbs free energyChemical compositionMineralogyChemistryMathematicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Viscosity is the single most important property governing the efficacy, rates, and nature of melt transport. Viscosity is intimately related to the structure and thermodynamics properties of the melts and is a reflection of the mechanisms of single atoms slipping over potential energy barriers. The ability to predict melt viscosity accurately is, therefore, of critical importance for gaining new insights into the structure of silicate melts. Simple composition melts, having a reduced number of components, offer an advantage for understanding the relationships between the chemical composition, structural organization and the rheological properties of a melt. Here we have compiled a large database of ∼970 experimental measurements of melt viscosity for the simple chemical systems MAS, CAS and MCAS. These data are used to create a single chemical model for predicting the non-Arrhenian viscosity as a function of temperature (T) and composition (X) across the entire MCAS system. The T-dependence of viscosity is accounted for by the three parameters in each of the model

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.036
GPT teacher head0.275
Teacher spread0.239 · 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 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

Citations27
Published2007
Admission routes1
Has abstractyes

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