Ultramafic melt viscosity: A model
Bibliographic record
Abstract
A non-Arrhenian model for the Newtonian viscosity (η) of ultramafic melts is presented. The model predicts the viscosity of ultramafic melts as a function of temperature (T), pressure (P), H2O content and for a range of melt compositions (70 < Mg# < 100). The calibration consists of 63 viscosity measurements at ambient pressure for 20 individual melt compositions and 5 high-P measurements on a single melt composition, all drawn from the literature. The data span 14 orders of magnitude of η (10−2 to 1011.8 Pa s), a T range of 880 to 2700 K, pressures from 1 atm to 25 GPa, and include measurements on hydrous melts containing 0.2 to 4.4 wt.% H2O. The T-dependence of viscosity is modelled with the VFT equation [log η = A + B/(T(K) − C)] whereby A is assumed to be a common, high-T limit for these melt compositions (i.e. log η∞ = -5.4). The pressure and composition effects are parameterised in terms of 5 adjustable parameters in expanded forms of B and C. The viscosity model is continuous across T-P-composition space and can predict ancillary transport properties including glass transition temperatures (Tg) and melt fragility (m). Melt viscosity decreases markedly with increasing H2O content but increases significantly with increasing pressure and decreasing Mg# (i.e. higher Fe-content). We show strong systematic decreases in Tg and m with increasing H2O content whereas an increase in P causes a rise in Tg and decrease in m. The predictive capacity of this model for ultramafic melt viscosity makes it pertinent to the fields of volcanology, geophysics, petrology, and the material sciences. Moreover, it provides constraints on models of magma oceans on terrestrial planets and, the evolution of planetary atmospheres via magmatic degassing on exoplanets.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".