A model to calculate the viscosity of silicate melts
Why this work is in the frame
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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 AlO 1.5 is modeled to vary linearly as a function of the mole fractions of the basic oxides at constant SiO 2 mole fraction. Systems containing AlO 1.5 show a more or less pronounced viscosity maximum close to the charge compensating composition. This maximum is caused by some of the Al 3+ taking on the same structural role as Si 4+ , thereby participating in the formation of the silica network. The network-forming Al 3+ must remain associated with either one Na + , or two Al 3+ ions must remain associated with one Mg 2+ or Ca 2+ , in order to assure charge neutrality. To take this into account we introduce the associates NaAlO 2 , CaAl 2 O 4 and MgAl 2 O 4 that correspond to charge compensated network-forming Al 3+ . The Gibbs energy of formation of these associates determines the amount of Al 3+ that takes on the network-forming role. We assume the effect on viscosity of network-forming Al 3+ to be the same as Si 4+ and optimize the Gibbs energies of the associates to reproduce the experimental viscosity data. Viscosities in ternary silicate systems without AlO 1.5 are quantitatively predicted with no additional ternary model parameters. Ternary systems MeO x – SiO 2 – AlO 1.5 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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it