Insights on Hydrothermal‐Magmatic Interactions and Eruptive Processes at Poás Volcano (Costa Rica) From High‐Frequency Gas Monitoring and Drone Measurements
Bibliographic record
Abstract
Abstract Identification of unambiguous signals of volcanic unrest is crucial in hazard assessment. Processes leading to phreatic and phreatomagmatic eruptions remain poorly understood, inhibiting effective eruption forecasting. Our 5‐year gas record from Poás volcano, combined with geophysical data, reveals systematic behavior associated with hydrothermal‐magmatic eruptions. Three eruptive episodes are covered, each with distinct geochemical and geophysical characteristics. Periods with larger eruptions tend to be associated with stronger excursions in monitoring data, particularly in SO 2 /CO 2 and SO 2 flux. The explosive 2017 phreatomagmatic eruption was the largest eruption at Poás since 1953 and was preceded by dramatic changes in gas and geophysical parameters. The use of drones played a crucial role in gas monitoring during this eruptive period. Hydrothermal sealing and volatile accumulation, followed by top‐down reactivation of a shallow previously emplaced magma body upon seal failure, are proposed as important processes leading to and contributing to the explosivity of the 2017 eruption.
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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 | 0.001 |
| 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) | 0.000 | 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 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".