A calibrated database of Raman spectra for natural silicate glasses: implications for modelling melt physical properties
Bibliographic record
Abstract
Abstract The physical properties of silicate melts are of critical importance for understanding magmatic and volcanic processes on Earth and other planets. Most physical properties of melts are, ultimately, a consequence of the structural organization of the melt. Robust and fully generalizable strategies for the prediction of properties of naturally occurring melts as functions of composition, temperature, and pressure remain a challenging goal. Given the structural origin of macroscopic properties, Raman spectroscopy of glasses, which provides information on melt and glass structure, may provide a useful technique to understanding and quantify variations in macroscopic melt properties. Here, with the aim of providing a generalizable model for predicting the viscosity of silicate melts, we present the results of a Raman spectroscopy campaign performed on 30 anhydrous multicomponent silicate glasses resulting from quenching of remelted and homogenized volcanic rocks and synthetic equivalents. The sample suite comprises one of the largest databases of multicomponent melts for which (a) chemical compositions and (b) physical properties (i.e., viscosity, fragility, heat capacity, and glass transition temperature) are known. Raman spectra have been collected using green light sources at wavelengths of 532 nm. Spectra were collected on the same sample suite in four independent laboratories involving instruments from different manufacturers and, thus, using different spectrometers, detectors, and analytical conditions. Our results are also compared and integrated with published data on some of the same samples derived from two others setups using green light sources with 514.5 and 532 nm wavelegths. For the same sample, the Raman spectra acquired using different setups show different intensities and intensity ratios. However, a strategy based on the ratio between the low‐ and high‐wavenumber peaks (R) was developed to standardize the data to normalized Raman ratios (Rn) and thus to remove interlaboratory differences. Using these advances, we predict melt viscosity solely with the use of Raman spectral measurements of multicomponent silicate glasses, thus demonstrating the potential of the method in describing physical properties of silicate melts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".