Bubble size distributions and magma-water interaction at Eyjafjallajökull volcano, Iceland
Bibliographic record
Abstract
Volcanic eruptions have large impacts on society. The 2010 eruption at Eyjafjallajökull volcano, Iceland had major consequences for air traffic and industry due to the large amounts of ash produced. This study explores the relationship between magma-water interaction and 3-dimensional (3-D) bubble size distributions in volcanic rocks with the aim of investigating if and how power law exponents in these distributions can be correlated to the degree of explosivity of a volcanic eruption. Results obtained from Eyjafjallajökull were compared to those at Stromboli volcano, Italy, where extensive research has been conducted. Normal strombolian activity reflects power law exponents of approximately 1 whereas more explosive paroxysmal eruptions show power law exponents of approximately 1.5. Power law exponents for natural scoria samples from Eyjafjallajökull are of approximately 0.8, which is more similar to normal strombolian activity than to paroxysmal events. These lower power law exponents are not be attributed to magma-water interaction present during the eruption, because experiments simulating this environment do not show any relation between power law exponents and magma-water interaction. The power law exponents obtained for Eyjafjallajökull are likely a reflection upon the eruption mechanisms at shallow levels, similar to typical strombolian activity. This similarity with strombolian activity suggests that during the time the samples were erupted, Eyjafjallajökull was being constantly fed by new magma that was mixing in with older magma in a shallow chamber.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".