Effect of alkalinity on sulfur concentration at sulfide saturation in hydrous basaltic andesite to shoshonite melts at 1270 °C and 1 GPa
Bibliographic record
Abstract
We have measured the effect of alkalis on S concentration at sulfide saturation (SCSS) in an underexplored compositional space of natural hydrous arc melts (basaltic andesite to shoshonite) at 1270 °C and 1 GPa. At an oxygen fugacity approximately 2.5 log units below the fayalite-magnetite-quartz (FMQ) buffer, SCSS increases with Na 2 O (562 ppm S/wt% Na 2 O), K 2 O (98 ppm S/wt% K 2 O), and total alkalis (88 ppm S/wt% Na 2 O+K 2 O) over the compositional range we have studied (1.6–3.1 wt% Na 2 O; 0–6.5 wt% K 2 O; 1.9–6.3 wt% FeO tot ). Experiments with ~1.3 wt% H 2 O show approximately half the increase in SCSS with alkalinity compared to those with ~3.0 wt% H 2 O. Our results show a possible limit to the increase in SCSS solely by increasing alkali concentration at ~7.5 wt% total alkali concentration. Using our results and published data, we retrained earlier SCSS models to provide a better fit to test data. We also developed a new empirical model using theoretical optical basicity as a compositional parameter that predicts SCSS in the overall data set with slightly better accuracy compared to previous models: In(SCSSppm)=16.34− 5784T− 339.4PT+10.85ln(Λ )+3.750XFeO+6.703XH2O$$\begin{array}{} \displaystyle \rm{In(SCSS_{ppm}) = 16.34 - \frac{5784}{{\it T}}-339.4 \frac{{\it P}}{{\it T}} + 10.85 ln (\Lambda)+ 3.750 {\it X}_{FeO} + 6.703 {\it X}_{H_{2}O}} \end{array}$$ with temperature ( T ) in Kelvin, pressure ( P ) in GPa, the optical basicity (Λ) and mole fractions ( X ) of FeO (calculated from Kress and Carmichael 1991), and H 2 O in the melt. The discrepancies between observed and predicted SCSS for our experiments of varying alkalinity reflects the heavy bias toward anhydrous, alkali-poor basalt compositions in the underlying data sets on which most models are developed.
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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 | 0.001 |
| 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".