Vapor Transport and Deposition of Cu-Sn-Co-Ag Alloys in Vesicles in Mafic Volcanic Rocks
Bibliographic record
Abstract
Abstract Metallic sublimates coated by sulfides and chlorides line the vesicle walls of mafic volcanic lava and bombs from Kīlauea, Vesuvius, Etna, and Stromboli. The metallic sublimates were morphologically and compositionally similar among the volcanoes. The highest concentrations of S and Cl occurred on the surface of the sublimates, while internally they had less than 1 wt % S and Cl in most cases, leading us to classify them as alloys. The major components of the alloys were Cu, Sn, Co, and Ag based on electron microprobe analyses and environmental scanning electron microscope element maps. Alloy element maps showed a covariance of Cu-Sn, while Co and Ag concentrations varied independently. Laser ablation-inductively coupled plasma-mass spectrometry analysis of matrix glass and melt inclusions in bombs from Stromboli showed appreciable amounts of Cu, Co, and Sn. We propose a model for the origin of the metallic grains, which involves syneruptive and posteruptive magma degassing and subsequent cooling of the basalt vesicles. During syneruptive vapor phase exsolution, volatile metals (Cu, Co, and Sn) partition into the vapor along with their ligands, S and Cl. The apparent oxygen fugacity (fO2) in these vapor bubbles is low because of the relative enrichment of the exsolved gas phase in H2 relative to H2O in silicate melts, due to the much higher diffusivity of the former in silicate melts. The high fH2 and low fO2 induces the precipitation of metal alloys from the vapor phase. Subsequently, the reducing environment in the vesicle dissipates as the cooling vapor oxidizes and as H2 diffuses away. Then, metal-rich sulfides (and chlorides) condense onto the outer surfaces of the metal alloy grains either due to a decrease in temperature or an increase in fO2. These alloys provide important insights into the partitioning of metals into a magmatic volatile phase at low pressure and high temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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 teacher head, 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".