Toward continuous 4D microgravity monitoring of volcanoes
Bibliographic record
Abstract
Abstract Four-dimensional or time-lapse microgravity monitoring has been used effectively on volcanoes for decades to characterize the changes in subsurface volcanic systems. With measurements typically lasting from a few days to weeks and then repeated a year later, the spatial resolution of theses studies is often at the expense of temporal resolution and vice versa. Continuous gravity studies with one to two instruments operating for a short period of time (weeks to months) have shown enticing evidence of very rapid changes in the volcanic plumbing system (minutes to hours) and in one case precursory signals leading to eruptive activity were detected. The need for true multi-instrument networks is clear if we are to have both the temporal and spatial reso-lution needed for effective volcano monitoring. However, the high cost of these instruments is currently limiting the implementation of continuous microgravity networks. An interim approach to consider is the development of a collaborative network of researchers able to bring multiple instruments together at key volcanoes to investigate multitemporal physical changes in a few type volcanoes. However, to truly move forward, it is imperative that new low-cost instruments are developed to increase the number of instruments available at a single site. Only in this way can both the temporal and spatial integrity of monitoring be maintained. Integration of these instruments into a multiparameter network of continuously recording sensors is essential for effective volcano monitoring and hazard mitigation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".