Quantification of the water content in synthetic glasses and natural melt inclusions using confocal Raman spectroscopy
Bibliographic record
Abstract
The importance of water in controlling magmatic ore-forming processes cannot be understated.To accurately constrain the water content in these systems, the application of confocal 532 nm laser Raman spectroscopy was evaluated on silicate glasses of varying bulk composition and water content with a final goal of testing the method developed on hydrous melt inclusions from natural samples.Water derivation is ultimately based on the ratio between areas of the silicate region at 700-1250 cm -1 and the O-H region at ~3600 cm -1 of the Raman spectra.Calibration of this method was carried out using hydrous synthetic glasses of rhyolitic, dacitic, and trachytic compositions with a range in water contents (2.68 to 6.59 wt% H2O).This study identified important steps for spectral treatment in the water quantification process, including baseline correction of the spectra and application of the frequency-temperature correction.The largest source of error for this determination was identified as a combination of glass sample heterogeneity and variations in the baseline correction of the spectra.The phenomenon of fluorescence, in coloured or impure glasses, obscures the water band and makes baseline corrections difficult.This was investigated through comparison of hydrous and anhydrous synthetic glasses of the same composition to evaluate a correction protocol, and the use of near-UV excitation sources to reduce fluorescence.Finally, this method was applied to natural, quartz-hosted melt inclusions from Late Paleozoic rhyolites from Southern New Brunswick.Ultimately, the method developed enables constraining of water content to within an average ~0.85 wt% accuracy for both synthetic glasses and natural melt inclusions.This is sufficient to differentiate between degassed vs. undegassed liquids, or melts trapped at contrasting crustal depths.
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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 | 0.000 | 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) | 0.001 | 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".