A model to calculate the viscosity of silicate melts
Bibliographic record
Abstract
Abstract A model has been developed that links the viscosities of silicate melts to their thermodynamic properties. Over the past several years, through critical evaluation of all available thermodynamic and phase equilibrium data, we have developed a quantitative thermodynamic description of multicomponent silicate melts using the Modified Quasichemical Model for short-range ordering. The local structure of the liquid, in terms of the bridging behavior of oxygen, calculated using our thermodynamic model allows us to characterize the structure of the liquid semi-quantitatively using the concepts of Q-species and connectivity of Q-species. The viscosity is modeled by optimizing viscosity parameters that are related to the structure of the liquid. The viscosity of pure liquid silica is modeled using four model parameters and every other unary liquid is modeled using two. The viscosity of all binary liquids is reproduced within experimental accuracy by optimizing one or at most two binary viscosity parameters for each system. In the present article the equations for the viscosity model are derived and analyses for the experimentally well-established systems CaO – SiO2 MgO – SiO2, NaO0.5 – SiO2, KO0.5 – SiO2 and AlO1.5 – SiO2 are presented. This is the first step in the development of a predictive model for the viscosity of multicomponent silicate melts that will be presented in part II.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".