A model to calculate the viscosity of silicate melts
Bibliographic record
Abstract
Abstract Our recently developed model for the viscosity of borosilicate melts is extended to describe and predict the viscosities of melts containing alkali oxides. In addition to the two model parameters that are required for each B2O3–MO x melt, where MO x is a basic oxide, three more parameters are needed when MO x is an alkali oxide to account for the formation of clusters near the tetraborate composition. The additional parameters represent the size and Gibbs energy of formation of these clusters and their contribution to the activation energy of the viscous flow. A general algorithm for the calculation of the viscosity is presented which summarizes the application of the viscosity model to melts that can contain two network formers, SiO2 and B2O3, any basic oxide and amphoteric oxides exhibiting the Charge Compensation Effect such as Al2O3. The predictive ability of the model is tested on all ternary subsystems of the B2O3–Na2O–K2O–CaO–MgO–PbO–ZnO–Al2O3–SiO2 system containing both an alkali oxide and B2O3 for which experimental data are available and on several multicomponent glass-forming melts around commercial glass compositions.
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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 | 0.000 |
| 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".