The Kuril Earthquakes and tsunamis of November 15, 2006, and January 13, 2007: Observations, analysis, and numerical modeling
Bibliographic record
Abstract
Major earthquakes occurred in the region of the Central Kuril Islands on November 15, 2006 (M w = 8.3) and January 13, 2007 (M w = 8.1). These earthquakes generated strong tsunamis recorded throughout the entire Pacific Ocean. The first was the strongest trans-Pacific tsunami of the past 42 years (since the Alaska tsunami in 1964). The high probability of a strong earthquake (M w ≥ 8.5) and associated destructive tsunami occurring in this region was predicted earlier. The most probable earthquake source region was investigated and possible scenarios for the tsunami generation were modeled. Investigations of the events that occurred on November 15, 2006, and January 13, 2007, enabled us to estimate the validity of the forecast and compare the parameters of the forecasted and observed earthquakes and tsunamis. In this paper, we discuss the concept of “seismic gaps,” which formed the basis for the forecast of these events, and put forward further assumptions about the expected seismic activity in the region. We investigate the efficiency of the tsunami warning services and estimate the statistical parameters for the observed tsunami waves that struck the Far Eastern coast of Russia and Northern Japan. The propagation and transformation of the 2006 and 2007 tsunamis are studied using numerical hydrodynamic modeling. The spatial characteristics of the two events are compared.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".