Analog experiments in volcanology: towards multimethod, upscaled and integrated models
Bibliographic record
Abstract
For decades scaled analog experiments have improved the understanding of a broad range of multiphase volcanological processes in controlled laboratory environments. Successfully modeled processes include magma flow through magma reservoirs, conduits and sheets, associated crustal deformation, lava flow, volcanic plume dynamics, ash cloud dispersion, pyroclast sedimentation, pyroclastic density currents and debris flows. Prior to the advent of computational modeling in volcanology, analog experiments were the primary method used to test newly developed concepts. Over the past two decades, technological advances have led to increased quantification of model observables, including deformation fields, lava flow rheologies, bubble and particle suspension compositions, runout distances, plume geometries, and rates of ash cloud spreading and sedimentation. For experimental results to yield further insights in volcanic processes and observables directly useful to volcano monitoring efforts, we expect future progress to focus on three major fronts: 1) improved multimethod measurements in experiments; 2) upscaling to near-natural-scale experiments conducted by multidisciplinary teams at internationally shared facilities; and 3) integration with computational models that will guide future geophysical observations and predictions of volcanic activity. This way, analog experiments will bridge gaps between other techniques in volcanology and improve our understanding and forecasting of volcanic activity from the Earth’s mantle to the surface and into the atmosphere.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".