Monitoring and forecasting fault development at actively forming calderas: An experimental study
Bibliographic record
Abstract
Caldera collapse events can be sudden and violent in the case of large explosivevolcanic eruptions or incremental in the case of long-lived eruptions. Faults nucleatingduring collapse are associated with seismic activity, but can also host potentialeconomic resources. Yet the kinematic behavior of newly formed faults is poorlyconstrained. We conducted a series of novel sandbox experiments using piezoelectricsensors to monitor stress perturbations during a caldera collapse. We found excellentspatial and temporal correlations among (a) fault nucleation, inferred from the stresssensor data, (b) the appearance of faults on the surface, and (c) final fault structure,obtained via cross-sections. We estimated fault propagation rates for early inner faultsand found that these rates increase with increasing magma evacuation rates. Weapplied our experimental results to seismic data from natural caldera-forming episodesin order to estimate rates of fault propagation for these systems. Our experiments are consistent with en masse caldera collapse events, such as at Katmai in 1912 and Pinatubo in 1991.
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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 | 0.002 | 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) | 0.000 | 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".