A model to calculate the viscosity of silicate melts
Bibliographic record
Abstract
Abstract Our recently developed model to describe the viscosity of binary silicate melts is extended to describe and predict the viscosities of multicomponent silicate melts. The viscosity of multicomponent melts containing no AlO1.5 is modeled to vary linearly as a function of the mole fractions of the basic oxides at constant SiO2 mole fraction. Systems containing AlO1.5 show a more or less pronounced viscosity maximum close to the charge compensating composition. This maximum is caused by some of the Al3+ taking on the same structural role as Si4+, thereby participating in the formation of the silica network. The network-forming Al3+ must remain associated with either one Na+, or two Al3+ ions must remain associated with one Mg2+ or Ca2+, in order to assure charge neutrality. To take this into account we introduce the associates NaAlO2, CaAl2O4 and MgAl2O4 that correspond to charge compensated network-forming Al3+. The Gibbs energy of formation of these associates determines the amount of Al3+ that takes on the network-forming role. We assume the effect on viscosity of network-forming Al3+ to be the same as Si4+ and optimize the Gibbs energies of the associates to reproduce the experimental viscosity data. Viscosities in ternary silicate systems without AlO1.5 are quantitatively predicted with no additional ternary model parameters. Ternary systems MeO x – SiO2 – AlO1.5 are modeled with only two temperature-independent ternary parameters per system. The model not only reproduces the magnitude of the observed viscosity maximum, but also its complex shape, asymmetry and temperature dependence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".