A model to calculate the viscosity of silicate melts
Bibliographic record
Abstract
Abstract Our recently developed model to describe the viscosity of silicate melts is extended to describe and predict the viscosities of alkali-rich silicate melts. The model requires one additional binary parameter for each M 2 O–SiO 2 system, where M is an alkali metal, to a total of three binary parameters per binary system alkali oxide – silica. In addition to unary and binary parameters, the model requires two ternary parameters for each alumina-containing ternary system M O x –Al 2 O 3 –SiO 2 , where M O x is a basic oxide, to describe the viscosity maxima in these ternary systems due to the Charge Compensation Effect. The viscosity of multicomponent melts and of ternary melts M O x – N O y –SiO 2 , where M O x and N O y are basic oxides, is predicted by the model solely from the unary, binary and ternary parameters. The available viscosity data for the alkali-containing subsystems of the Al 2 O 3 –CaO–MgO–Na 2 O–K 2 O–SiO 2 system are reviewed. The model reproduces the experimental data for binary and ternary melts and predicts the viscosities of multicomponent melts within experimental error limits. In particular, the viscosities of glass melts and melts of importance for petrology are well predicted by the model.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".