Buoyant replenishment in silicic magma reservoirs: Experimental approach and implications for magma dynamics, crystal mush remobilization, and eruption
Bibliographic record
Abstract
We present new experiments on replenishment of rhyolite magma chambers by rhyolite magma using corn syrup‐water solutions. We emphasize small density contrasts and show that buoyancy is the key controlling factor for whether injections will rise to the top (if buoyant) or pond at the base (if denser). During emplacement, we observe little or no mixing of the injected liquid with the reservoir liquid, as predicted by the fact that our injections have low Reynolds numbers (<10, typically). At later stages, the low‐buoyancy (≤1 kg m−3) injected liquid, which has accumulated at the top of the reservoir, undergoes mixing with the reservoir liquid, which may originate by the gravitational destabilization of a thin layer of denser resident liquid trapped above the injected liquid layer. The presence of a basal crystal mush, modeled by acrylic beads in a corn syrup‐water solution matrix is also considered. Slightly buoyant injections entrain a small fraction of mush particles to the top of the overlying liquid layer. Entrainment efficiency increases dramatically for high‐buoyancy injections. We hypothesize that the injected liquid can entrain a maximum quantity of mush particles, which corresponds to the amount required for the injected liquid/mush particle suspension to attain neutral buoyancy in the resident liquid. Hence for silicic systems, a replenishing melt can entrain up to 12.5% crystals during its ascent through the mush. Our results have implications for rhyolites bearing crystals with disequilibrium features, as they may represent mush crystals remobilized by a replenishing silicic magma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".