Combining the All-Source Green's Functions and the GPS-Derived Source Functions for Fast Tsunami Predictions—Illustrated by the March 2011 Japan Tsunami
Bibliographic record
Abstract
Abstract This paper proposes an effective approach on how to predict tsunamis rapidly following a submarine earthquake by combining a real-time GPS-derived tsunami source function with a set of precalculated all-source Green's functions (ASGFs). The approach uses the data from both teleseismic and coastal GPS networks to constrain a tsunami source function consisting of both sea surface elevation and horizontal velocity field, and uses the ASGFs to instantaneously transfer the source function to the arrival time series at the destination points. The ASGF can take a tsunami source of arbitrary geographic origin and resolve it as fine as the native resolution of a tsunami propagation model from which the ASGF is derived. This new approach is verified by the 2011 Tohoku tsunami using data measured by the Deep-Ocean Assessment and Reporting of Tsunamis (DART) buoys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".