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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".