Acoustic surveillance for hazardous eruptions (ASHE)
Bibliographic record
Abstract
The potential of using infrasound to rapidly identify explosive volcanic eruptions has been discussed in the environmental acoustics and aviation safety communities for some time. The ability of sounds in the 0.01–10 Hz range to propagate for long distances with little attenuation suggests broad-area regional monitoring with a modest number of observing sites is possible. The ASHE experiment tests both the practical utility of infrasound as a regional-scale volcanic eruption detection tool, and the feasibility of using such an infrasound system to provide timely operational alerts to aviation through Volcanic Ash Advisory Centres (VAACs). Several infrasound arrays are deployed in a volcanic region, sending data in real time to a central detector, and onward to participating VAACs for comparison with existing warning systems. The ASHE experiment will determine if infrasound can complement both seismic and satellite observations to improve monitoring of volcanic hazard. Continuous acoustic surveillance can reduce the ambiguity between eruptive and purely seismic activity in an active volcano and provide additional estimates for the onset time of an eruption. The onset time estimates can be used as triggers for ash transport models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".