The challenge of installing a tsunami early warning system in the vicinity of the Sunda Arc, Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Indonesia is located along the most prominent active continental margin in the Indian Ocean, the so-called Sunda Arc and, therefore, is one of the most threatened regions of the world in terms of natural hazards such as earthquakes, volcanoes, and tsunamis. On 26 December 2004 the third largest earthquake ever instrumentally recorded (magnitude 9.3, Stein and Okal, 2005) occurred off-shore northern Sumatra and triggered a mega-tsunami affecting the whole Indian Ocean. Almost a quarter of a million people were killed, as the region was not prepared either in terms of early-warning or in terms of disaster response. In order to be able to provide, in future, a fast and reliable warning procedure for the population, Germany, immediately after the catastrophe, offered during the UN World Conference on Disaster Reduction in Kobe, Hyogo/Japan in January 2005 technical support for the development and installation of a tsunami early warning system for the Indian Ocean in addition to assistance in capacity building in particular for local communities. This offer was accepted by Indonesia but also by other countries like Sri Lanka, the Maldives and some East-African countries. Anyhow the main focus of our activities has been carried out in Indonesia as the main source of tsunami threat for the entire Indian Ocean. Challenging for the technical concept of this warning system are the extremely short warning times for Indonesia, due to its vicinity to the Sunda Arc. For this reason the German Indonesian Tsunami Early Warning System (GITEWS) integrates different modern and new scientific monitoring technologies and analysis methods.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it