Bibliographic record
Abstract
The Indian Ocean tsunami of 26 December 2004 was one of the greatest natural disasters; it was also the first internet-mediated natural disaster. Despite the presumed ubiquity and power of advanced technologies including satellites and the internet, no advance warning was given to the affected coastal populations by their governments or others. This article examines the conditions for the supply of effective early warnings of disasters, drawing from the experience of both the 26 December 2004 tsunami and the false warnings issued after another great earthquake in the Sunda Trench on 28 March 2005. The potential of information and communication technologies for prompt communication of hazard detection and monitoring information and for effective dissemination of alert and warning messages is examined. The factors contributing to the absence of institutions necessary for the realization of that potential are explored.
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.077 | 0.033 |
| Insufficient payload (model declined to judge) | 0.133 | 0.032 |
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".