Viscosity of Magmatic liquids: a model.
Bibliographic record
Abstract
The viscosity of silicate melts controls magma transport dynamics, eruption style and rates of physicochemical processes (e.g., degassing, crystallization) in natural magmas. Thus a comprehensive viscosity model for magmatic liquids has long been a goal of earth scientists. Here we present a model that predicts the non-Arrhenian Newtonian viscosity of silicate melts as a function of T and melt composition, including the rheologically important volatile constituents H2O and F. Ourmodel is based on >1770 measurements of viscosity on multicomponent anhydrous and volatile-rich silicate melts. The non-Arrhenian T-dependence of viscosity is accounted for by the VFT equation [log ?=A+B/(T(K)-C)]. The optimization assumes a common, high-T limit (A) for silicatemelt viscosity and returns a value for this limit of -4.55 (+0.2) (e.g., log ?~10-4.6 Pa s). All compositional dependence is ascribed to the parameters B and C and is accounted for by an additional 17model coefficients. Our model is continuous in composition- and temperature-space and predicts the viscosity of natural volatile-bearing silicate melts (SiO2, Al2O3, TiO2, FeOtot, CaO, MgO,MnO, Na2O, K2O, P2O5, H2O, F2O-1) over fifteen log units of viscosity (10-1–1014 Pa s). The model for viscosity can also predict other transport properties including glasstransition temperatures (Tg) and melt fragility (m). We show strong systematic decreases in Tg and m with increasing volatile content. This pattern has implications for predicting styles of volcanic eruption and understanding silicate melt structure. Our model transforms a quarter-century of experimental study of melt viscosities, into a parameterisation having a predictive capacity thatmakes it relevant to diverse fields of research including: volcanology, geophysics, petrology and material sciences.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".