Acoustic surveillance of hazardous volcanic eruptions (ASHE) in Asia
Bibliographic record
Abstract
The ASHE Ecuador (2004-2012) collaboration between Ecuador, Canada, and the United States demonstrated the capability to use real-time infrasound to provide low-latency volcanic eruption notifications to the Volcano Ash Advisory Center (VAAC) in Washington DC. The Atmospheric dynamics Research Infrastructure in Europe (ARISE, 2012-2018) supported by the European Commission fosters integrating innovative methods for remote detection and characterization of distant eruptive sources through collaborations with the VAAC Toulouse and the Comprehensive Nuclear-Test-Ban-Treaty Organization (CTBTO). The ASHE Asia project proposes an international collaboration between the Earth Observatory of Singapore, the VAAC Darwin, National Data Centers in Japan, Korea, and Palau, and will receive the support of ARISE, to provide improved early notification of potentially hazardous eruptions in Asia and the Western Pacific using a combination of established technologies and next-generation mobile sensing systems. The increased availability of open seismo-acoustic data in the ASEAN region as well as recent advances in mobile distributed sensors networks will facilitate unprecedented rapid progress in monitoring remote regions for early detection of hazardous volcanic eruptions and other natural disasters.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".