Understanding and forecasting sudden explosive eruptions
Bibliographic record
Abstract
Explosive eruptions of VEI ≤ 3 commonly occur with few warning signs. Such eruptions can be magmatic, phreatomagmatic, or phreatic in nature, and they are driven by the catastrophic release of pressurized gas. Our challenge is how to better forecast these eruptions and better understand them with existing and new tools. Here we examine a number of such eruptions, some lethal to humans, which have occurred during the last decade. We first describe key precursory signals that preceded these events, examine whether they developed in a bottom-up or top-down fashion, and compare the different timescales of precursory activity. In an attempt to understand how, when, and where these systems become pressurized, we then outline the different processes and crustal locations leading to the overpressure. We further identify a number of precursory signals that may be generally applicable and exportable to such systems, and we discuss effective means of using thresholds of these precursory signals and eruptive transitions to improve our forecasting abilities. We conclude by outlining three grand challenges for the next decade: (1) complete forecasts of explosive eruptions including when, where, how big, and what type, (2) a full view of subsurface volcano plumbing, and (3) monitoring networks that are comprehensive, similar, and systematic in nature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".